├── Data.csv ├── Data_v2.csv ├── README.md └── pandas.ipynb /Data.csv: -------------------------------------------------------------------------------- 1 | ,Name,Age,City,Degree,Grade 2 | person1,Ali,27,Tehran,MSC,83 3 | person2,Hosein,32,Esfahan,PHD,68 4 | person3,Sadeq,23,Mashhad,MA,45 5 | pesron4,Hamid,26,Tabriz,MA,72 6 | person5,Hamed,25,Qazvin,MSC,54 7 | peson6,Reza,30,Tehran,MSC,66 8 | person7,Taha,28,Babol,MSC,90 9 | person8,Amir,40,Tehran,PHD,81 10 | person9,Omid,35,Arak,PHD,76 11 | person10,Shayan,19,Mashhad,MA,38 12 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Training pandas 2 | 3 | `Pandas` is a popular Python library used for data analysis and manipulation. It provides powerful data structures like DataFrame and Series for handling structured data, along with a wide range of operations for combining, transforming, and analyzing data. 4 | 5 | In this section, we wanted to bring you most of the exercises available in `pandas`. 6 | We put the used data in csv format for you so that you can have more exercises on them 7 | 8 | ## Ways of communication 9 | 10 | Contact me in case of problems. 11 | 12 | - ashkan02011@gmail.com 13 | - [github](https://github.com/ashkan0201) 14 | -------------------------------------------------------------------------------- /pandas.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Pandas is a popular Python library used for data analysis and manipulation. It provides powerful data structures like DataFrame and Series for handling structured data, along with a wide range of operations for combining, transforming, and analyzing data." 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [ 15 | { 16 | "name": "stdout", 17 | "output_type": "stream", 18 | "text": [ 19 | "Requirement already satisfied: pandas in c:\\users\\user\\anaconda3\\lib\\site-packages (1.1.3)\n", 20 | "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2.8.1)\n", 21 | "Requirement already satisfied: numpy>=1.15.4 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (1.24.3)\n", 22 | "Requirement already satisfied: pytz>=2017.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas) (2020.1)\n", 23 | "Requirement already satisfied: six>=1.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n" 24 | ] 25 | } 26 | ], 27 | "source": [ 28 | "# Installation\n", 29 | "!pip install pandas" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 2, 35 | "metadata": {}, 36 | "outputs": [], 37 | "source": [ 38 | "import pandas as pd" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 3, 44 | "metadata": {}, 45 | "outputs": [ 46 | { 47 | "name": "stdout", 48 | "output_type": "stream", 49 | "text": [ 50 | "0 A\n", 51 | "1 s\n", 52 | "2 h\n", 53 | "3 k\n", 54 | "4 a\n", 55 | "5 n\n", 56 | "dtype: object\n" 57 | ] 58 | } 59 | ], 60 | "source": [ 61 | "# The left part is our indexes and the right part is our data.\n", 62 | "\n", 63 | "# My Data\n", 64 | "data = [\"A\", \"s\", \"h\", \"k\", \"a\", \"n\"]\n", 65 | "\n", 66 | "result = pd.Series(data)\n", 67 | "\n", 68 | "print(result)" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 4, 74 | "metadata": {}, 75 | "outputs": [ 76 | { 77 | "name": "stdout", 78 | "output_type": "stream", 79 | "text": [ 80 | "1 A\n", 81 | "3 s\n", 82 | "5 h\n", 83 | "7 k\n", 84 | "9 a\n", 85 | "11 n\n", 86 | "dtype: object\n", 87 | "\n", 88 | "\n", 89 | "['A' 's' 'h' 'k' 'a' 'n']\n", 90 | "Int64Index([1, 3, 5, 7, 9, 11], dtype='int64')\n" 91 | ] 92 | } 93 | ], 94 | "source": [ 95 | "# Defining an arbitrary index on the data.\n", 96 | "\n", 97 | "# My Data\n", 98 | "data = [\"A\", \"s\", \"h\", \"k\", \"a\", \"n\"]\n", 99 | "\n", 100 | "result = pd.Series(data, index=[1, 3, 5, 7, 9, 11])\n", 101 | "\n", 102 | "print(result)\n", 103 | "print(\"\\n\")\n", 104 | "print(result.values)\n", 105 | "print(result.index)" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 5, 111 | "metadata": {}, 112 | "outputs": [ 113 | { 114 | "name": "stdout", 115 | "output_type": "stream", 116 | "text": [ 117 | "A s h k a n\n", 118 | "A s h k a n\n" 119 | ] 120 | } 121 | ], 122 | "source": [ 123 | "# Indexing in the list.\n", 124 | "# In this section, if you define an index for your data, when you get the index,\n", 125 | "#you must give exactly the index of the same values that you defined for the index in the serial argument.\n", 126 | "\n", 127 | "# My Data\n", 128 | "data = [\"A\", \"s\", \"h\", \"k\", \"a\", \"n\"]\n", 129 | "\n", 130 | "result = pd.Series(data, index=[\"in0\", \"in1\", \"in2\", \"in3\", \"in4\", \"in5\"])\n", 131 | "\n", 132 | "print(result[\"in0\"], result[\"in1\"], result[\"in2\"], result[\"in3\"], result[\"in4\"], result[\"in5\"])\n", 133 | "print(result[0], result[1], result[2], result[3], result[4], result[5])" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": 6, 139 | "metadata": {}, 140 | "outputs": [ 141 | { 142 | "name": "stdout", 143 | "output_type": "stream", 144 | "text": [ 145 | "ten 10\n", 146 | "twenty 20\n", 147 | "thirty 30\n", 148 | "forty 40\n", 149 | "dtype: int64\n", 150 | "\n", 151 | "\n", 152 | "ten 10\n", 153 | "twenty 20\n", 154 | "thirty 30\n", 155 | "forty 40\n", 156 | "dtype: int64\n" 157 | ] 158 | } 159 | ], 160 | "source": [ 161 | "# Slicing with Pandaz functions.\n", 162 | "\n", 163 | "# My Data\n", 164 | "data = [10, 20, 30, 40, 50, 60]\n", 165 | "\n", 166 | "result = pd.Series(data, index=[\"ten\", \"twenty\", \"thirty\", \"forty\", \"fifty\", \"sixty\"])\n", 167 | "\n", 168 | "# Indexing with the iloc function means to get the index from the data itself\n", 169 | "print(result.iloc[:4])\n", 170 | "\n", 171 | "print(\"\\n\")\n", 172 | "\n", 173 | "# Indexing with the loc function means to take from the defined index itself\n", 174 | "print(result.loc[\"ten\":\"forty\"])" 175 | ] 176 | }, 177 | { 178 | "cell_type": "code", 179 | "execution_count": 7, 180 | "metadata": {}, 181 | "outputs": [ 182 | { 183 | "name": "stdout", 184 | "output_type": "stream", 185 | "text": [ 186 | "ten 10\n", 187 | "twenty 20\n", 188 | "thirty 30\n", 189 | "forty 40\n", 190 | "dtype: int64\n", 191 | "\n", 192 | "\n", 193 | "ten 10\n", 194 | "twenty 20\n", 195 | "thirty 30\n", 196 | "forty 40\n", 197 | "dtype: int64\n" 198 | ] 199 | } 200 | ], 201 | "source": [ 202 | "# Slicing\n", 203 | "\n", 204 | "# My Data\n", 205 | "data = [10, 20, 30, 40, 50, 60]\n", 206 | "\n", 207 | "result = pd.Series(data, index=[\"ten\", \"twenty\", \"thirty\", \"forty\", \"fifty\", \"sixty\"])\n", 208 | "print(result[\"ten\":\"forty\"])\n", 209 | "print(\"\\n\")\n", 210 | "print(result[0:4])" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 8, 216 | "metadata": {}, 217 | "outputs": [ 218 | { 219 | "name": "stdout", 220 | "output_type": "stream", 221 | "text": [ 222 | "a Ashkan\n", 223 | "b Python\n", 224 | "c Pandas\n", 225 | "dtype: object\n" 226 | ] 227 | } 228 | ], 229 | "source": [ 230 | "# In this section, the data is in the form of a dictionary.\n", 231 | "# The key in this section is the index and the value is our data.\n", 232 | "\n", 233 | "# My Data\n", 234 | "data = {\n", 235 | " \"a\":\"Ashkan\",\n", 236 | " \"b\":\"Python\",\n", 237 | " \"c\":\"Pandas\"\n", 238 | "}\n", 239 | "\n", 240 | "result = pd.Series(data)\n", 241 | "print(result)" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": 9, 247 | "metadata": {}, 248 | "outputs": [ 249 | { 250 | "name": "stdout", 251 | "output_type": "stream", 252 | "text": [ 253 | "Ashkan Python Pandas nan\n", 254 | "Ashkan Python Pandas nan\n" 255 | ] 256 | } 257 | ], 258 | "source": [ 259 | "# Define arbitrary index\n", 260 | "# Pay attention, when you define an index in this section,\n", 261 | "# the index values must be equal to the dictionary key values\n", 262 | "\n", 263 | "\n", 264 | "# My Data\n", 265 | "data = {\n", 266 | " \"a\":\"Ashkan\",\n", 267 | " \"b\":\"Python\",\n", 268 | " \"c\":\"Pandas\"\n", 269 | "}\n", 270 | "\n", 271 | "result = pd.Series(data, index=[\"a\", \"b\", \"c\", \"d\"])\n", 272 | "\n", 273 | "# Getting data with index in two ways:\n", 274 | "\n", 275 | "# 1\n", 276 | "print(result[\"a\"], result[\"b\"], result[\"c\"] ,result[\"d\"])\n", 277 | "\n", 278 | "# 2\n", 279 | "print(result[0], result[1], result[2], result[3])" 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "execution_count": null, 285 | "metadata": {}, 286 | "outputs": [], 287 | "source": [] 288 | }, 289 | { 290 | "cell_type": "markdown", 291 | "metadata": {}, 292 | "source": [ 293 | "### Mathematical calculations in pandaz." 294 | ] 295 | }, 296 | { 297 | "cell_type": "code", 298 | "execution_count": 10, 299 | "metadata": {}, 300 | "outputs": [ 301 | { 302 | "name": "stdout", 303 | "output_type": "stream", 304 | "text": [ 305 | "Addition\n", 306 | "0 8585\n", 307 | "1 [85, 85]\n", 308 | "2 171.7\n", 309 | "3 8585\n", 310 | "4 170\n", 311 | "dtype: object\n", 312 | "\n", 313 | "Subtraction\n", 314 | "0 -3\n", 315 | "1 -1\n", 316 | "2 1\n", 317 | "3 3\n", 318 | "dtype: int64\n", 319 | "\n", 320 | "Distribution\n", 321 | "0 0.250000\n", 322 | "1 0.666667\n", 323 | "2 1.500000\n", 324 | "3 4.000000\n", 325 | "dtype: float64\n", 326 | "\n", 327 | "Multiplication\n", 328 | "0 4\n", 329 | "1 6\n", 330 | "2 6\n", 331 | "3 4\n", 332 | "dtype: int64\n", 333 | "\n", 334 | "Power\n", 335 | "0 1\n", 336 | "1 8\n", 337 | "2 9\n", 338 | "3 4\n", 339 | "dtype: int64\n" 340 | ] 341 | } 342 | ], 343 | "source": [ 344 | "# My Data\n", 345 | "data1 = [\"85\", [85], 85.85, (\"85\"), 85]\n", 346 | "data2 = [\"85\", [85], 85.85, (\"85\"), 85]\n", 347 | "data3 = [1, 2, 3, 4]\n", 348 | "data4 = [4, 3, 2, 1]\n", 349 | "\n", 350 | "ser1 = pd.Series(data1)\n", 351 | "ser2 = pd.Series(data2)\n", 352 | "ser3 = pd.Series(data3)\n", 353 | "ser4 = pd.Series(data4)\n", 354 | "\n", 355 | "print(\"Addition\")\n", 356 | "print(ser1.add(ser2))\n", 357 | "\n", 358 | "print(\"\\nSubtraction\")\n", 359 | "print(ser3.sub(ser4))\n", 360 | "\n", 361 | "print(\"\\nDistribution\")\n", 362 | "print(ser3.div(ser4))\n", 363 | "\n", 364 | "print(\"\\nMultiplication\")\n", 365 | "print(ser3.mul(ser4))\n", 366 | "\n", 367 | "print(\"\\nPower\")\n", 368 | "print(ser3.pow(ser4))" 369 | ] 370 | }, 371 | { 372 | "cell_type": "code", 373 | "execution_count": 11, 374 | "metadata": {}, 375 | "outputs": [ 376 | { 377 | "data": { 378 | "text/html": [ 379 | "
\n", 380 | "\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 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | "
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" 428 | ], 429 | "text/plain": [ 430 | " column1\n", 431 | "0 A\n", 432 | "1 s\n", 433 | "2 h\n", 434 | "3 k\n", 435 | "4 a\n", 436 | "5 n" 437 | ] 438 | }, 439 | "execution_count": 11, 440 | "metadata": {}, 441 | "output_type": "execute_result" 442 | } 443 | ], 444 | "source": [ 445 | "# Name the column\n", 446 | "\n", 447 | "# My Data\n", 448 | "data = [\"A\", \"s\", \"h\", \"k\", \"a\", \"n\"]\n", 449 | "\n", 450 | "result = pd.DataFrame(data, columns=[\"column1\"])\n", 451 | "\n", 452 | "result" 453 | ] 454 | }, 455 | { 456 | "cell_type": "code", 457 | "execution_count": 12, 458 | "metadata": {}, 459 | "outputs": [ 460 | { 461 | "data": { 462 | "text/html": [ 463 | "
\n", 464 | "\n", 477 | "\n", 478 | " \n", 479 | " \n", 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 | "
NameAgeId
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" 508 | ], 509 | "text/plain": [ 510 | " Name Age Id\n", 511 | "0 Ashkan 19 872634\n", 512 | "1 Sara 20 981239\n", 513 | "2 Donya 18 918243" 514 | ] 515 | }, 516 | "execution_count": 12, 517 | "metadata": {}, 518 | "output_type": "execute_result" 519 | } 520 | ], 521 | "source": [ 522 | "# Name the column in the dictionary\n", 523 | "\n", 524 | "# My Data\n", 525 | "data = {\n", 526 | " \"Name\":[\"Ashkan\", \"Sara\", \"Donya\"],\n", 527 | " \"Age\":[19, 20, 18],\n", 528 | " \"Id\":[872634, 981239, 918243]\n", 529 | "}\n", 530 | "# Keys are column names and values are table rows\n", 531 | "result = pd.DataFrame(data)\n", 532 | "\n", 533 | "result" 534 | ] 535 | }, 536 | { 537 | "cell_type": "code", 538 | "execution_count": 13, 539 | "metadata": {}, 540 | "outputs": [ 541 | { 542 | "data": { 543 | "text/html": [ 544 | "
\n", 545 | "\n", 558 | "\n", 559 | " \n", 560 | " \n", 561 | " \n", 562 | " \n", 563 | " \n", 564 | " \n", 565 | " \n", 566 | " \n", 567 | " \n", 568 | " \n", 569 | " \n", 570 | " \n", 571 | " \n", 572 | " \n", 573 | " \n", 574 | " \n", 575 | " \n", 576 | " \n", 577 | " \n", 578 | " \n", 579 | " \n", 580 | " \n", 581 | " \n", 582 | " \n", 583 | "
NameId
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" 585 | ], 586 | "text/plain": [ 587 | " Name Id\n", 588 | "0 Ashkan 872634\n", 589 | "1 Sara 981239\n", 590 | "2 Donya 918243" 591 | ] 592 | }, 593 | "execution_count": 13, 594 | "metadata": {}, 595 | "output_type": "execute_result" 596 | } 597 | ], 598 | "source": [ 599 | "# Column indexing\n", 600 | "\n", 601 | "# My Data\n", 602 | "data = {\n", 603 | " \"Name\":[\"Ashkan\", \"Sara\", \"Donya\"],\n", 604 | " \"Age\":[19, 20, 18],\n", 605 | " \"Id\":[872634, 981239, 918243]\n", 606 | "}\n", 607 | "\n", 608 | "result = pd.DataFrame(data)\n", 609 | "\n", 610 | "result[[\"Name\", \"Id\"]]" 611 | ] 612 | }, 613 | { 614 | "cell_type": "code", 615 | "execution_count": 14, 616 | "metadata": {}, 617 | "outputs": [ 618 | { 619 | "name": "stdout", 620 | "output_type": "stream", 621 | "text": [ 622 | "Name Ashkan\n", 623 | "Age 19\n", 624 | "Id 872634\n", 625 | "Name: 0, dtype: object\n", 626 | "\n", 627 | "\n", 628 | "Name Ashkan\n", 629 | "Age 19\n", 630 | "Id 872634\n", 631 | "Name: 0, dtype: object\n", 632 | "\n", 633 | "\n", 634 | " Name Age Id\n", 635 | "0 Ashkan 19 872634\n", 636 | "\n", 637 | "\n", 638 | " Name Age Id\n", 639 | "0 Ashkan 19 872634\n" 640 | ] 641 | } 642 | ], 643 | "source": [ 644 | "# Row indexing\n", 645 | "\n", 646 | "# My Data\n", 647 | "data = {\n", 648 | " \"Name\":[\"Ashkan\", \"Sara\", \"Donya\"],\n", 649 | " \"Age\":[19, 20, 18],\n", 650 | " \"Id\":[872634, 981239, 918243]\n", 651 | "}\n", 652 | "\n", 653 | "result = pd.DataFrame(data, index=[\"0\", \"1\", \"2\"])\n", 654 | "\n", 655 | "# Vertical\n", 656 | "print(result.loc[\"0\"])\n", 657 | "print(\"\\n\")\n", 658 | "print(result.iloc[0])\n", 659 | "\n", 660 | "print(\"\\n\")\n", 661 | "\n", 662 | "# Horizontal\n", 663 | "print(result.loc[[\"0\"]])\n", 664 | "print(\"\\n\")\n", 665 | "print(result.iloc[[0]])" 666 | ] 667 | }, 668 | { 669 | "cell_type": "code", 670 | "execution_count": 15, 671 | "metadata": {}, 672 | "outputs": [ 673 | { 674 | "name": "stdout", 675 | "output_type": "stream", 676 | "text": [ 677 | " Name Age Id\n", 678 | "1 Sara 20 981239\n", 679 | "2 Donya 18 918243\n", 680 | "\n", 681 | "\n", 682 | " Name Age Id\n", 683 | "1 Sara 20 981239\n", 684 | "2 Donya 18 918243\n" 685 | ] 686 | } 687 | ], 688 | "source": [ 689 | "# Row Slicing\n", 690 | "\n", 691 | "# My Data\n", 692 | "data = {\n", 693 | " \"Name\":[\"Ashkan\", \"Sara\", \"Donya\"],\n", 694 | " \"Age\":[19, 20, 18],\n", 695 | " \"Id\":[872634, 981239, 918243]\n", 696 | "}\n", 697 | "\n", 698 | "result = pd.DataFrame(data, index=[\"0\", \"1\", \"2\"])\n", 699 | "\n", 700 | "# 1\n", 701 | "print(result.loc[\"1\":\"2\"])\n", 702 | "\n", 703 | "print(\"\\n\")\n", 704 | "\n", 705 | "# 2\n", 706 | "print(result.iloc[1:])" 707 | ] 708 | }, 709 | { 710 | "cell_type": "code", 711 | "execution_count": 16, 712 | "metadata": {}, 713 | "outputs": [ 714 | { 715 | "name": "stdout", 716 | "output_type": "stream", 717 | "text": [ 718 | " Name Age\n", 719 | "0 Ashkan 19\n", 720 | "1 Sara 20\n", 721 | "\n", 722 | "\n", 723 | " Name Age\n", 724 | "0 Ashkan 19\n", 725 | "1 Sara 20\n" 726 | ] 727 | } 728 | ], 729 | "source": [ 730 | "# Slashing rows and columns together\n", 731 | "\n", 732 | "# My Data\n", 733 | "data = {\n", 734 | " \"Name\":[\"Ashkan\", \"Sara\", \"Donya\"],\n", 735 | " \"Age\":[19, 20, 18],\n", 736 | " \"Id\":[872634, 981239, 918243]\n", 737 | "}\n", 738 | "\n", 739 | "result = pd.DataFrame(data, index=[\"0\", \"1\", \"2\"])\n", 740 | "\n", 741 | "# The right part of the bracket is for columns and the other part for rows\n", 742 | "\n", 743 | "# 1\n", 744 | "print(result.loc[\"0\":\"1\", \"Name\":\"Age\"])\n", 745 | "\n", 746 | "print(\"\\n\")\n", 747 | "\n", 748 | "# 2\n", 749 | "print(result.iloc[0:2, 0:2])" 750 | ] 751 | }, 752 | { 753 | "cell_type": "code", 754 | "execution_count": 17, 755 | "metadata": {}, 756 | "outputs": [ 757 | { 758 | "name": "stdout", 759 | "output_type": "stream", 760 | "text": [ 761 | "0 Ashkan\n", 762 | "1 Sara\n", 763 | "2 Donya\n", 764 | "Name: Name, dtype: object \n", 765 | "\n", 766 | "0 True\n", 767 | "1 False\n", 768 | "2 False\n", 769 | "Name: Name, dtype: bool \n", 770 | "\n", 771 | "0 False\n", 772 | "1 True\n", 773 | "2 False\n", 774 | "Name: Age, dtype: bool \n", 775 | "\n" 776 | ] 777 | } 778 | ], 779 | "source": [ 780 | "# Comparative\n", 781 | "\n", 782 | "# My Data\n", 783 | "data = {\n", 784 | " \"Name\":[\"Ashkan\", \"Sara\", \"Donya\"],\n", 785 | " \"Age\":[19, 20, 18],\n", 786 | " \"Id\":[872634, 981239, 918243]\n", 787 | "}\n", 788 | "\n", 789 | "result = pd.DataFrame(data, index=[\"0\", \"1\", \"2\"])\n", 790 | "\n", 791 | "# 1\n", 792 | "print(result[\"Name\"], \"\\n\")\n", 793 | "\n", 794 | "# 2\n", 795 | "print(result[\"Name\"] == \"Ashkan\", \"\\n\")\n", 796 | "\n", 797 | "# 3\n", 798 | "print(result[\"Age\"] > 19, \"\\n\")" 799 | ] 800 | }, 801 | { 802 | "cell_type": "code", 803 | "execution_count": 18, 804 | "metadata": {}, 805 | "outputs": [ 806 | { 807 | "name": "stdout", 808 | "output_type": "stream", 809 | "text": [ 810 | " Name Age Id\n", 811 | "0 Ashkan 19 872634 \n", 812 | "\n", 813 | " Name Age Id\n", 814 | "1 Sara 20 981239\n" 815 | ] 816 | } 817 | ], 818 | "source": [ 819 | "# Query with condition.\n", 820 | "\n", 821 | "# My Data\n", 822 | "data = {\n", 823 | " \"Name\":[\"Ashkan\", \"Sara\", \"Donya\"],\n", 824 | " \"Age\":[19, 20, 18],\n", 825 | " \"Id\":[872634, 981239, 918243]\n", 826 | "}\n", 827 | "\n", 828 | "result = pd.DataFrame(data, index=[\"0\", \"1\", \"2\"])\n", 829 | "\n", 830 | "# 1\n", 831 | "print(result[result[\"Name\"] == \"Ashkan\"], \"\\n\")\n", 832 | "\n", 833 | "# 2\n", 834 | "print(result[result.index == \"1\"])" 835 | ] 836 | }, 837 | { 838 | "cell_type": "code", 839 | "execution_count": 19, 840 | "metadata": {}, 841 | "outputs": [ 842 | { 843 | "name": "stdout", 844 | "output_type": "stream", 845 | "text": [ 846 | " Name Age Id\n", 847 | "0 True False True\n", 848 | "1 False False False\n", 849 | "2 False False False \n", 850 | "\n", 851 | " Name Age Id\n", 852 | "0 False True False\n", 853 | "1 True True True\n", 854 | "2 True True True \n", 855 | "\n", 856 | " Name Age Id\n", 857 | "0 NaN 19 NaN\n", 858 | "1 Sara 20 981239.0\n", 859 | "2 Donya 18 918243.0\n" 860 | ] 861 | } 862 | ], 863 | "source": [ 864 | "# Null values\n", 865 | "\n", 866 | "# My Data\n", 867 | "data = {\n", 868 | " \"Name\":[None, \"Sara\", \"Donya\"],\n", 869 | " \"Age\":[19, 20, 18],\n", 870 | " \"Id\":[None, 981239, 918243]\n", 871 | "}\n", 872 | "\n", 873 | "result = pd.DataFrame(data)\n", 874 | "\n", 875 | "# Null\n", 876 | "print(result.isnull(), \"\\n\")\n", 877 | "\n", 878 | "# Not null\n", 879 | "print(result.notnull(), \"\\n\")\n", 880 | "\n", 881 | "# Where are the nulls?\n", 882 | "print(result[result.notnull()])" 883 | ] 884 | }, 885 | { 886 | "cell_type": "code", 887 | "execution_count": 20, 888 | "metadata": {}, 889 | "outputs": [ 890 | { 891 | "data": { 892 | "text/html": [ 893 | "
\n", 894 | "\n", 907 | "\n", 908 | " \n", 909 | " \n", 910 | " \n", 911 | " \n", 912 | " \n", 913 | " \n", 914 | " \n", 915 | " \n", 916 | " \n", 917 | " \n", 918 | " \n", 919 | " \n", 920 | " \n", 921 | " \n", 922 | " \n", 923 | " \n", 924 | " \n", 925 | " \n", 926 | " \n", 927 | " \n", 928 | " \n", 929 | " \n", 930 | " \n", 931 | " \n", 932 | " \n", 933 | " \n", 934 | " \n", 935 | " \n", 936 | "
NameAgeId
0Im not null!19Im not null!
1Sara20981239
2Donya18918243
\n", 937 | "
" 938 | ], 939 | "text/plain": [ 940 | " Name Age Id\n", 941 | "0 Im not null! 19 Im not null!\n", 942 | "1 Sara 20 981239\n", 943 | "2 Donya 18 918243" 944 | ] 945 | }, 946 | "execution_count": 20, 947 | "metadata": {}, 948 | "output_type": "execute_result" 949 | } 950 | ], 951 | "source": [ 952 | "# Assign values to null values.\n", 953 | "\n", 954 | "# My Data\n", 955 | "data = {\n", 956 | " \"Name\":[None, \"Sara\", \"Donya\"],\n", 957 | " \"Age\":[19, 20, 18],\n", 958 | " \"Id\":[None, 981239, 918243]\n", 959 | "}\n", 960 | "\n", 961 | "result = pd.DataFrame(data)\n", 962 | "\n", 963 | "result.fillna(\"Im not null!\")" 964 | ] 965 | }, 966 | { 967 | "cell_type": "markdown", 968 | "metadata": {}, 969 | "source": [ 970 | "# To remove null values!\n", 971 | "### result.dropna()" 972 | ] 973 | }, 974 | { 975 | "cell_type": "code", 976 | "execution_count": null, 977 | "metadata": {}, 978 | "outputs": [], 979 | "source": [] 980 | }, 981 | { 982 | "cell_type": "markdown", 983 | "metadata": {}, 984 | "source": [ 985 | "# Work with csv files." 986 | ] 987 | }, 988 | { 989 | "cell_type": "code", 990 | "execution_count": 21, 991 | "metadata": {}, 992 | "outputs": [], 993 | "source": [ 994 | "result = pd.read_csv(\"Data.csv\")" 995 | ] 996 | }, 997 | { 998 | "cell_type": "code", 999 | "execution_count": 22, 1000 | "metadata": {}, 1001 | "outputs": [ 1002 | { 1003 | "data": { 1004 | "text/html": [ 1005 | "
\n", 1006 | "\n", 1019 | "\n", 1020 | " \n", 1021 | " \n", 1022 | " \n", 1023 | " \n", 1024 | " \n", 1025 | " \n", 1026 | " \n", 1027 | " \n", 1028 | " \n", 1029 | " \n", 1030 | " \n", 1031 | " \n", 1032 | " \n", 1033 | " \n", 1034 | " \n", 1035 | " \n", 1036 | " \n", 1037 | " \n", 1038 | " \n", 1039 | " \n", 1040 | " \n", 1041 | " \n", 1042 | " \n", 1043 | " \n", 1044 | " \n", 1045 | " \n", 1046 | " \n", 1047 | " \n", 1048 | " \n", 1049 | " \n", 1050 | " \n", 1051 | " \n", 1052 | " \n", 1053 | " \n", 1054 | " \n", 1055 | " \n", 1056 | " \n", 1057 | " \n", 1058 | " \n", 1059 | " \n", 1060 | " \n", 1061 | " \n", 1062 | " \n", 1063 | " \n", 1064 | " \n", 1065 | " \n", 1066 | " \n", 1067 | " \n", 1068 | " \n", 1069 | " \n", 1070 | " \n", 1071 | " \n", 1072 | " \n", 1073 | " \n", 1074 | " \n", 1075 | " \n", 1076 | " \n", 1077 | " \n", 1078 | " \n", 1079 | " \n", 1080 | " \n", 1081 | " \n", 1082 | " \n", 1083 | " \n", 1084 | " \n", 1085 | " \n", 1086 | " \n", 1087 | " \n", 1088 | " \n", 1089 | " \n", 1090 | " \n", 1091 | " \n", 1092 | " \n", 1093 | " \n", 1094 | " \n", 1095 | " \n", 1096 | " \n", 1097 | " \n", 1098 | " \n", 1099 | " \n", 1100 | " \n", 1101 | " \n", 1102 | " \n", 1103 | " \n", 1104 | " \n", 1105 | " \n", 1106 | " \n", 1107 | " \n", 1108 | " \n", 1109 | " \n", 1110 | " \n", 1111 | " \n", 1112 | " \n", 1113 | " \n", 1114 | " \n", 1115 | " \n", 1116 | " \n", 1117 | " \n", 1118 | " \n", 1119 | " \n", 1120 | " \n", 1121 | " \n", 1122 | " \n", 1123 | "
Unnamed: 0NameAgeCityDegreeGrade
0person1Ali27TehranMSC83
1person2Hosein32EsfahanPHD68
2person3Sadeq23MashhadMA45
3pesron4Hamid26TabrizMA72
4person5Hamed25QazvinMSC54
5peson6Reza30TehranMSC66
6person7Taha28BabolMSC90
7person8Amir40TehranPHD81
8person9Omid35ArakPHD76
9person10Shayan19MashhadMA38
\n", 1124 | "
" 1125 | ], 1126 | "text/plain": [ 1127 | " Unnamed: 0 Name Age City Degree Grade\n", 1128 | "0 person1 Ali 27 Tehran MSC 83\n", 1129 | "1 person2 Hosein 32 Esfahan PHD 68\n", 1130 | "2 person3 Sadeq 23 Mashhad MA 45\n", 1131 | "3 pesron4 Hamid 26 Tabriz MA 72\n", 1132 | "4 person5 Hamed 25 Qazvin MSC 54\n", 1133 | "5 peson6 Reza 30 Tehran MSC 66\n", 1134 | "6 person7 Taha 28 Babol MSC 90\n", 1135 | "7 person8 Amir 40 Tehran PHD 81\n", 1136 | "8 person9 Omid 35 Arak PHD 76\n", 1137 | "9 person10 Shayan 19 Mashhad MA 38" 1138 | ] 1139 | }, 1140 | "execution_count": 22, 1141 | "metadata": {}, 1142 | "output_type": "execute_result" 1143 | } 1144 | ], 1145 | "source": [ 1146 | "result" 1147 | ] 1148 | }, 1149 | { 1150 | "cell_type": "code", 1151 | "execution_count": 23, 1152 | "metadata": {}, 1153 | "outputs": [ 1154 | { 1155 | "data": { 1156 | "text/html": [ 1157 | "
\n", 1158 | "\n", 1171 | "\n", 1172 | " \n", 1173 | " \n", 1174 | " \n", 1175 | " \n", 1176 | " \n", 1177 | " \n", 1178 | " \n", 1179 | " \n", 1180 | " \n", 1181 | " \n", 1182 | " \n", 1183 | " \n", 1184 | " \n", 1185 | " \n", 1186 | " \n", 1187 | " \n", 1188 | " \n", 1189 | " \n", 1190 | " \n", 1191 | " \n", 1192 | " \n", 1193 | " \n", 1194 | " \n", 1195 | " \n", 1196 | " \n", 1197 | " \n", 1198 | " \n", 1199 | " \n", 1200 | " \n", 1201 | "
NameAge
0Ali27
1Hosein32
2Sadeq23
3Hamid26
\n", 1202 | "
" 1203 | ], 1204 | "text/plain": [ 1205 | " Name Age\n", 1206 | "0 Ali 27\n", 1207 | "1 Hosein 32\n", 1208 | "2 Sadeq 23\n", 1209 | "3 Hamid 26" 1210 | ] 1211 | }, 1212 | "execution_count": 23, 1213 | "metadata": {}, 1214 | "output_type": "execute_result" 1215 | } 1216 | ], 1217 | "source": [ 1218 | "# Indexing in csv files.\n", 1219 | "result.iloc[0:4,1:3]" 1220 | ] 1221 | }, 1222 | { 1223 | "cell_type": "code", 1224 | "execution_count": 24, 1225 | "metadata": {}, 1226 | "outputs": [ 1227 | { 1228 | "data": { 1229 | "text/html": [ 1230 | "
\n", 1231 | "\n", 1244 | "\n", 1245 | " \n", 1246 | " \n", 1247 | " \n", 1248 | " \n", 1249 | " \n", 1250 | " \n", 1251 | " \n", 1252 | " \n", 1253 | " \n", 1254 | " \n", 1255 | " \n", 1256 | " \n", 1257 | " \n", 1258 | " \n", 1259 | " \n", 1260 | " \n", 1261 | " \n", 1262 | " \n", 1263 | " \n", 1264 | " \n", 1265 | " \n", 1266 | " \n", 1267 | " \n", 1268 | " \n", 1269 | " \n", 1270 | " \n", 1271 | " \n", 1272 | " \n", 1273 | " \n", 1274 | " \n", 1275 | " \n", 1276 | " \n", 1277 | " \n", 1278 | " \n", 1279 | " \n", 1280 | " \n", 1281 | " \n", 1282 | " \n", 1283 | " \n", 1284 | " \n", 1285 | " \n", 1286 | " \n", 1287 | " \n", 1288 | " \n", 1289 | " \n", 1290 | " \n", 1291 | " \n", 1292 | " \n", 1293 | " \n", 1294 | " \n", 1295 | " \n", 1296 | " \n", 1297 | " \n", 1298 | " \n", 1299 | " \n", 1300 | " \n", 1301 | " \n", 1302 | " \n", 1303 | "
Unnamed: 0NameAgeCityDegreeGrade
0person1Ali27TehranMSC83
1person2Hosein32EsfahanPHD68
2person3Sadeq23MashhadMA45
3pesron4Hamid26TabrizMA72
4person5Hamed25QazvinMSC54
\n", 1304 | "
" 1305 | ], 1306 | "text/plain": [ 1307 | " Unnamed: 0 Name Age City Degree Grade\n", 1308 | "0 person1 Ali 27 Tehran MSC 83\n", 1309 | "1 person2 Hosein 32 Esfahan PHD 68\n", 1310 | "2 person3 Sadeq 23 Mashhad MA 45\n", 1311 | "3 pesron4 Hamid 26 Tabriz MA 72\n", 1312 | "4 person5 Hamed 25 Qazvin MSC 54" 1313 | ] 1314 | }, 1315 | "execution_count": 24, 1316 | "metadata": {}, 1317 | "output_type": "execute_result" 1318 | } 1319 | ], 1320 | "source": [ 1321 | "# Reading the first 5 members.\n", 1322 | "result.head()" 1323 | ] 1324 | }, 1325 | { 1326 | "cell_type": "code", 1327 | "execution_count": 25, 1328 | "metadata": {}, 1329 | "outputs": [ 1330 | { 1331 | "data": { 1332 | "text/html": [ 1333 | "
\n", 1334 | "\n", 1347 | "\n", 1348 | " \n", 1349 | " \n", 1350 | " \n", 1351 | " \n", 1352 | " \n", 1353 | " \n", 1354 | " \n", 1355 | " \n", 1356 | " \n", 1357 | " \n", 1358 | " \n", 1359 | " \n", 1360 | " \n", 1361 | " \n", 1362 | " \n", 1363 | " \n", 1364 | " \n", 1365 | " \n", 1366 | " \n", 1367 | " \n", 1368 | " \n", 1369 | " \n", 1370 | " \n", 1371 | " \n", 1372 | " \n", 1373 | " \n", 1374 | " \n", 1375 | " \n", 1376 | " \n", 1377 | " \n", 1378 | " \n", 1379 | "
Unnamed: 0NameAgeCityDegreeGrade
0person1Ali27TehranMSC83
1person2Hosein32EsfahanPHD68
\n", 1380 | "
" 1381 | ], 1382 | "text/plain": [ 1383 | " Unnamed: 0 Name Age City Degree Grade\n", 1384 | "0 person1 Ali 27 Tehran MSC 83\n", 1385 | "1 person2 Hosein 32 Esfahan PHD 68" 1386 | ] 1387 | }, 1388 | "execution_count": 25, 1389 | "metadata": {}, 1390 | "output_type": "execute_result" 1391 | } 1392 | ], 1393 | "source": [ 1394 | "# Reading the first n term.\n", 1395 | "result.head(2)" 1396 | ] 1397 | }, 1398 | { 1399 | "cell_type": "code", 1400 | "execution_count": 26, 1401 | "metadata": {}, 1402 | "outputs": [ 1403 | { 1404 | "data": { 1405 | "text/html": [ 1406 | "
\n", 1407 | "\n", 1420 | "\n", 1421 | " \n", 1422 | " \n", 1423 | " \n", 1424 | " \n", 1425 | " \n", 1426 | " \n", 1427 | " \n", 1428 | " \n", 1429 | " \n", 1430 | " \n", 1431 | " \n", 1432 | " \n", 1433 | " \n", 1434 | " \n", 1435 | " \n", 1436 | " \n", 1437 | " \n", 1438 | " \n", 1439 | " \n", 1440 | " \n", 1441 | " \n", 1442 | " \n", 1443 | " \n", 1444 | " \n", 1445 | " \n", 1446 | " \n", 1447 | " \n", 1448 | " \n", 1449 | " \n", 1450 | " \n", 1451 | " \n", 1452 | " \n", 1453 | " \n", 1454 | " \n", 1455 | " \n", 1456 | " \n", 1457 | " \n", 1458 | " \n", 1459 | " \n", 1460 | " \n", 1461 | " \n", 1462 | " \n", 1463 | " \n", 1464 | " \n", 1465 | " \n", 1466 | " \n", 1467 | " \n", 1468 | " \n", 1469 | " \n", 1470 | " \n", 1471 | " \n", 1472 | " \n", 1473 | " \n", 1474 | " \n", 1475 | " \n", 1476 | " \n", 1477 | " \n", 1478 | " \n", 1479 | "
Unnamed: 0NameAgeCityDegreeGrade
5peson6Reza30TehranMSC66
6person7Taha28BabolMSC90
7person8Amir40TehranPHD81
8person9Omid35ArakPHD76
9person10Shayan19MashhadMA38
\n", 1480 | "
" 1481 | ], 1482 | "text/plain": [ 1483 | " Unnamed: 0 Name Age City Degree Grade\n", 1484 | "5 peson6 Reza 30 Tehran MSC 66\n", 1485 | "6 person7 Taha 28 Babol MSC 90\n", 1486 | "7 person8 Amir 40 Tehran PHD 81\n", 1487 | "8 person9 Omid 35 Arak PHD 76\n", 1488 | "9 person10 Shayan 19 Mashhad MA 38" 1489 | ] 1490 | }, 1491 | "execution_count": 26, 1492 | "metadata": {}, 1493 | "output_type": "execute_result" 1494 | } 1495 | ], 1496 | "source": [ 1497 | "# Reading the last 5 members.\n", 1498 | "result.tail()" 1499 | ] 1500 | }, 1501 | { 1502 | "cell_type": "code", 1503 | "execution_count": 27, 1504 | "metadata": {}, 1505 | "outputs": [ 1506 | { 1507 | "data": { 1508 | "text/html": [ 1509 | "
\n", 1510 | "\n", 1523 | "\n", 1524 | " \n", 1525 | " \n", 1526 | " \n", 1527 | " \n", 1528 | " \n", 1529 | " \n", 1530 | " \n", 1531 | " \n", 1532 | " \n", 1533 | " \n", 1534 | " \n", 1535 | " \n", 1536 | " \n", 1537 | " \n", 1538 | " \n", 1539 | " \n", 1540 | " \n", 1541 | " \n", 1542 | " \n", 1543 | " \n", 1544 | " \n", 1545 | " \n", 1546 | " \n", 1547 | " \n", 1548 | " \n", 1549 | " \n", 1550 | " \n", 1551 | " \n", 1552 | " \n", 1553 | " \n", 1554 | " \n", 1555 | "
Unnamed: 0NameAgeCityDegreeGrade
8person9Omid35ArakPHD76
9person10Shayan19MashhadMA38
\n", 1556 | "
" 1557 | ], 1558 | "text/plain": [ 1559 | " Unnamed: 0 Name Age City Degree Grade\n", 1560 | "8 person9 Omid 35 Arak PHD 76\n", 1561 | "9 person10 Shayan 19 Mashhad MA 38" 1562 | ] 1563 | }, 1564 | "execution_count": 27, 1565 | "metadata": {}, 1566 | "output_type": "execute_result" 1567 | } 1568 | ], 1569 | "source": [ 1570 | "# Read the last n member.\n", 1571 | "result.tail(2)" 1572 | ] 1573 | }, 1574 | { 1575 | "cell_type": "code", 1576 | "execution_count": 28, 1577 | "metadata": {}, 1578 | "outputs": [], 1579 | "source": [ 1580 | "# Save data in a csv file.\n", 1581 | "\n", 1582 | "# My Data\n", 1583 | "data = {\n", 1584 | " \"Name\":[\"Ashkan\", \"Sara\", \"Donya\"],\n", 1585 | " \"Age\":[19, 20, 18],\n", 1586 | " \"Id\":[878128, 981239, 918243]\n", 1587 | "}\n", 1588 | "\n", 1589 | "result = pd.DataFrame(data)\n", 1590 | "\n", 1591 | "result.to_csv(\"SAVED_DATA.csv\", index=None)" 1592 | ] 1593 | }, 1594 | { 1595 | "cell_type": "code", 1596 | "execution_count": 29, 1597 | "metadata": {}, 1598 | "outputs": [], 1599 | "source": [ 1600 | "# Reading a series of data from a csv file.\n", 1601 | "\n", 1602 | "result = pd.read_csv(\"Data.csv\")" 1603 | ] 1604 | }, 1605 | { 1606 | "cell_type": "code", 1607 | "execution_count": 30, 1608 | "metadata": {}, 1609 | "outputs": [ 1610 | { 1611 | "data": { 1612 | "text/plain": [ 1613 | "(10, 6)" 1614 | ] 1615 | }, 1616 | "execution_count": 30, 1617 | "metadata": {}, 1618 | "output_type": "execute_result" 1619 | } 1620 | ], 1621 | "source": [ 1622 | "# To find out the number of rows and columns.\n", 1623 | "result.shape" 1624 | ] 1625 | }, 1626 | { 1627 | "cell_type": "code", 1628 | "execution_count": 31, 1629 | "metadata": {}, 1630 | "outputs": [ 1631 | { 1632 | "data": { 1633 | "text/plain": [ 1634 | "Unnamed: 0 10\n", 1635 | "Name 10\n", 1636 | "Age 10\n", 1637 | "City 10\n", 1638 | "Degree 10\n", 1639 | "Grade 10\n", 1640 | "dtype: int64" 1641 | ] 1642 | }, 1643 | "execution_count": 31, 1644 | "metadata": {}, 1645 | "output_type": "execute_result" 1646 | } 1647 | ], 1648 | "source": [ 1649 | "# To find out the number of values.\n", 1650 | "result.count()" 1651 | ] 1652 | }, 1653 | { 1654 | "cell_type": "code", 1655 | "execution_count": 32, 1656 | "metadata": {}, 1657 | "outputs": [ 1658 | { 1659 | "name": "stdout", 1660 | "output_type": "stream", 1661 | "text": [ 1662 | "['person1' 'Ali' 27 'Tehran' 'MSC' 83]\n", 1663 | "['person2' 'Hosein' 32 'Esfahan' 'PHD' 68]\n", 1664 | "['person3' 'Sadeq' 23 'Mashhad' 'MA' 45]\n", 1665 | "['pesron4' 'Hamid' 26 'Tabriz' 'MA' 72]\n", 1666 | "['person5' 'Hamed' 25 'Qazvin' 'MSC' 54]\n", 1667 | "['peson6' 'Reza' 30 'Tehran' 'MSC' 66]\n", 1668 | "['person7' 'Taha' 28 'Babol' 'MSC' 90]\n", 1669 | "['person8' 'Amir' 40 'Tehran' 'PHD' 81]\n", 1670 | "['person9' 'Omid' 35 'Arak' 'PHD' 76]\n", 1671 | "['person10' 'Shayan' 19 'Mashhad' 'MA' 38]\n" 1672 | ] 1673 | } 1674 | ], 1675 | "source": [ 1676 | "# Convert data to list.\n", 1677 | "for i in result.values:\n", 1678 | " print(i)" 1679 | ] 1680 | }, 1681 | { 1682 | "cell_type": "code", 1683 | "execution_count": 33, 1684 | "metadata": {}, 1685 | "outputs": [ 1686 | { 1687 | "data": { 1688 | "text/html": [ 1689 | "
\n", 1690 | "\n", 1703 | "\n", 1704 | " \n", 1705 | " \n", 1706 | " \n", 1707 | " \n", 1708 | " \n", 1709 | " \n", 1710 | " \n", 1711 | " \n", 1712 | " \n", 1713 | " \n", 1714 | " \n", 1715 | " \n", 1716 | " \n", 1717 | " \n", 1718 | " \n", 1719 | " \n", 1720 | " \n", 1721 | " \n", 1722 | " \n", 1723 | " \n", 1724 | " \n", 1725 | " \n", 1726 | " \n", 1727 | " \n", 1728 | " \n", 1729 | " \n", 1730 | " \n", 1731 | " \n", 1732 | " \n", 1733 | " \n", 1734 | " \n", 1735 | " \n", 1736 | " \n", 1737 | " \n", 1738 | " \n", 1739 | " \n", 1740 | " \n", 1741 | " \n", 1742 | " \n", 1743 | " \n", 1744 | " \n", 1745 | " \n", 1746 | " \n", 1747 | " \n", 1748 | " \n", 1749 | " \n", 1750 | " \n", 1751 | " \n", 1752 | " \n", 1753 | "
AgeGrade
count10.00000010.000000
mean28.50000067.300000
std6.05988616.938123
min19.00000038.000000
25%25.25000057.000000
50%27.50000070.000000
75%31.50000079.750000
max40.00000090.000000
\n", 1754 | "
" 1755 | ], 1756 | "text/plain": [ 1757 | " Age Grade\n", 1758 | "count 10.000000 10.000000\n", 1759 | "mean 28.500000 67.300000\n", 1760 | "std 6.059886 16.938123\n", 1761 | "min 19.000000 38.000000\n", 1762 | "25% 25.250000 57.000000\n", 1763 | "50% 27.500000 70.000000\n", 1764 | "75% 31.500000 79.750000\n", 1765 | "max 40.000000 90.000000" 1766 | ] 1767 | }, 1768 | "execution_count": 33, 1769 | "metadata": {}, 1770 | "output_type": "execute_result" 1771 | } 1772 | ], 1773 | "source": [ 1774 | "# Get statistical data.\n", 1775 | "result.describe()" 1776 | ] 1777 | }, 1778 | { 1779 | "cell_type": "code", 1780 | "execution_count": 34, 1781 | "metadata": {}, 1782 | "outputs": [ 1783 | { 1784 | "data": { 1785 | "text/html": [ 1786 | "
\n", 1787 | "\n", 1800 | "\n", 1801 | " \n", 1802 | " \n", 1803 | " \n", 1804 | " \n", 1805 | " \n", 1806 | " \n", 1807 | " \n", 1808 | " \n", 1809 | " \n", 1810 | " \n", 1811 | " \n", 1812 | " \n", 1813 | " \n", 1814 | " \n", 1815 | " \n", 1816 | " \n", 1817 | " \n", 1818 | " \n", 1819 | " \n", 1820 | " \n", 1821 | " \n", 1822 | " \n", 1823 | " \n", 1824 | " \n", 1825 | " \n", 1826 | " \n", 1827 | " \n", 1828 | " \n", 1829 | " \n", 1830 | " \n", 1831 | " \n", 1832 | " \n", 1833 | " \n", 1834 | " \n", 1835 | " \n", 1836 | " \n", 1837 | " \n", 1838 | " \n", 1839 | " \n", 1840 | " \n", 1841 | " \n", 1842 | " \n", 1843 | " \n", 1844 | " \n", 1845 | " \n", 1846 | " \n", 1847 | " \n", 1848 | " \n", 1849 | " \n", 1850 | " \n", 1851 | " \n", 1852 | " \n", 1853 | " \n", 1854 | " \n", 1855 | " \n", 1856 | " \n", 1857 | " \n", 1858 | " \n", 1859 | " \n", 1860 | " \n", 1861 | " \n", 1862 | " \n", 1863 | " \n", 1864 | " \n", 1865 | " \n", 1866 | " \n", 1867 | " \n", 1868 | " \n", 1869 | " \n", 1870 | " \n", 1871 | " \n", 1872 | " \n", 1873 | " \n", 1874 | " \n", 1875 | " \n", 1876 | " \n", 1877 | " \n", 1878 | " \n", 1879 | " \n", 1880 | " \n", 1881 | " \n", 1882 | " \n", 1883 | " \n", 1884 | " \n", 1885 | " \n", 1886 | " \n", 1887 | " \n", 1888 | " \n", 1889 | " \n", 1890 | " \n", 1891 | " \n", 1892 | " \n", 1893 | " \n", 1894 | " \n", 1895 | " \n", 1896 | " \n", 1897 | " \n", 1898 | " \n", 1899 | " \n", 1900 | " \n", 1901 | "
Unnamed: 0AgeCityDegreeGrade
Name
Aliperson127TehranMSC83
Hoseinperson232EsfahanPHD68
Sadeqperson323MashhadMA45
Hamidpesron426TabrizMA72
Hamedperson525QazvinMSC54
Rezapeson630TehranMSC66
Tahaperson728BabolMSC90
Amirperson840TehranPHD81
Omidperson935ArakPHD76
Shayanperson1019MashhadMA38
\n", 1902 | "
" 1903 | ], 1904 | "text/plain": [ 1905 | " Unnamed: 0 Age City Degree Grade\n", 1906 | "Name \n", 1907 | "Ali person1 27 Tehran MSC 83\n", 1908 | "Hosein person2 32 Esfahan PHD 68\n", 1909 | "Sadeq person3 23 Mashhad MA 45\n", 1910 | "Hamid pesron4 26 Tabriz MA 72\n", 1911 | "Hamed person5 25 Qazvin MSC 54\n", 1912 | "Reza peson6 30 Tehran MSC 66\n", 1913 | "Taha person7 28 Babol MSC 90\n", 1914 | "Amir person8 40 Tehran PHD 81\n", 1915 | "Omid person9 35 Arak PHD 76\n", 1916 | "Shayan person10 19 Mashhad MA 38" 1917 | ] 1918 | }, 1919 | "execution_count": 34, 1920 | "metadata": {}, 1921 | "output_type": "execute_result" 1922 | } 1923 | ], 1924 | "source": [ 1925 | "# Set the index with your desired column.\n", 1926 | "result.set_index(\"Name\")" 1927 | ] 1928 | }, 1929 | { 1930 | "cell_type": "markdown", 1931 | "metadata": {}, 1932 | "source": [ 1933 | "# Ordering." 1934 | ] 1935 | }, 1936 | { 1937 | "cell_type": "code", 1938 | "execution_count": 35, 1939 | "metadata": {}, 1940 | "outputs": [ 1941 | { 1942 | "data": { 1943 | "text/html": [ 1944 | "
\n", 1945 | "\n", 1958 | "\n", 1959 | " \n", 1960 | " \n", 1961 | " \n", 1962 | " \n", 1963 | " \n", 1964 | " \n", 1965 | " \n", 1966 | " \n", 1967 | " \n", 1968 | " \n", 1969 | " \n", 1970 | " \n", 1971 | " \n", 1972 | " \n", 1973 | " \n", 1974 | " \n", 1975 | " \n", 1976 | " \n", 1977 | " \n", 1978 | " \n", 1979 | " \n", 1980 | " \n", 1981 | " \n", 1982 | " \n", 1983 | " \n", 1984 | " \n", 1985 | " \n", 1986 | " \n", 1987 | " \n", 1988 | " \n", 1989 | " \n", 1990 | " \n", 1991 | " \n", 1992 | " \n", 1993 | " \n", 1994 | " \n", 1995 | " \n", 1996 | " \n", 1997 | " \n", 1998 | " \n", 1999 | " \n", 2000 | " \n", 2001 | " \n", 2002 | " \n", 2003 | " \n", 2004 | " \n", 2005 | " \n", 2006 | " \n", 2007 | " \n", 2008 | " \n", 2009 | " \n", 2010 | " \n", 2011 | " \n", 2012 | " \n", 2013 | " \n", 2014 | " \n", 2015 | " \n", 2016 | " \n", 2017 | " \n", 2018 | " \n", 2019 | " \n", 2020 | " \n", 2021 | " \n", 2022 | " \n", 2023 | " \n", 2024 | " \n", 2025 | " \n", 2026 | " \n", 2027 | " \n", 2028 | " \n", 2029 | " \n", 2030 | " \n", 2031 | " \n", 2032 | " \n", 2033 | " \n", 2034 | " \n", 2035 | " \n", 2036 | " \n", 2037 | " \n", 2038 | " \n", 2039 | " \n", 2040 | " \n", 2041 | " \n", 2042 | " \n", 2043 | " \n", 2044 | " \n", 2045 | " \n", 2046 | " \n", 2047 | " \n", 2048 | " \n", 2049 | " \n", 2050 | " \n", 2051 | " \n", 2052 | " \n", 2053 | " \n", 2054 | " \n", 2055 | " \n", 2056 | " \n", 2057 | " \n", 2058 | " \n", 2059 | " \n", 2060 | " \n", 2061 | " \n", 2062 | "
Unnamed: 0NameAgeCityDegreeGrade
0person1Ali27TehranMSC83
7person8Amir40TehranPHD81
4person5Hamed25QazvinMSC54
3pesron4Hamid26TabrizMA72
1person2Hosein32EsfahanPHD68
8person9Omid35ArakPHD76
5peson6Reza30TehranMSC66
2person3Sadeq23MashhadMA45
9person10Shayan19MashhadMA38
6person7Taha28BabolMSC90
\n", 2063 | "
" 2064 | ], 2065 | "text/plain": [ 2066 | " Unnamed: 0 Name Age City Degree Grade\n", 2067 | "0 person1 Ali 27 Tehran MSC 83\n", 2068 | "7 person8 Amir 40 Tehran PHD 81\n", 2069 | "4 person5 Hamed 25 Qazvin MSC 54\n", 2070 | "3 pesron4 Hamid 26 Tabriz MA 72\n", 2071 | "1 person2 Hosein 32 Esfahan PHD 68\n", 2072 | "8 person9 Omid 35 Arak PHD 76\n", 2073 | "5 peson6 Reza 30 Tehran MSC 66\n", 2074 | "2 person3 Sadeq 23 Mashhad MA 45\n", 2075 | "9 person10 Shayan 19 Mashhad MA 38\n", 2076 | "6 person7 Taha 28 Babol MSC 90" 2077 | ] 2078 | }, 2079 | "execution_count": 35, 2080 | "metadata": {}, 2081 | "output_type": "execute_result" 2082 | } 2083 | ], 2084 | "source": [ 2085 | "# Sort data with your desired column.\n", 2086 | "result.sort_values(\"Name\")" 2087 | ] 2088 | }, 2089 | { 2090 | "cell_type": "code", 2091 | "execution_count": 36, 2092 | "metadata": {}, 2093 | "outputs": [ 2094 | { 2095 | "data": { 2096 | "text/html": [ 2097 | "
\n", 2098 | "\n", 2111 | "\n", 2112 | " \n", 2113 | " \n", 2114 | " \n", 2115 | " \n", 2116 | " \n", 2117 | " \n", 2118 | " \n", 2119 | " \n", 2120 | " \n", 2121 | " \n", 2122 | " \n", 2123 | " \n", 2124 | " \n", 2125 | " \n", 2126 | " \n", 2127 | " \n", 2128 | " \n", 2129 | " \n", 2130 | " \n", 2131 | " \n", 2132 | " \n", 2133 | " \n", 2134 | " \n", 2135 | " \n", 2136 | " \n", 2137 | " \n", 2138 | " \n", 2139 | " \n", 2140 | " \n", 2141 | " \n", 2142 | " \n", 2143 | " \n", 2144 | " \n", 2145 | " \n", 2146 | " \n", 2147 | " \n", 2148 | " \n", 2149 | " \n", 2150 | " \n", 2151 | " \n", 2152 | " \n", 2153 | " \n", 2154 | " \n", 2155 | " \n", 2156 | " \n", 2157 | " \n", 2158 | " \n", 2159 | " \n", 2160 | " \n", 2161 | " \n", 2162 | " \n", 2163 | " \n", 2164 | " \n", 2165 | " \n", 2166 | " \n", 2167 | " \n", 2168 | " \n", 2169 | " \n", 2170 | " \n", 2171 | " \n", 2172 | " \n", 2173 | " \n", 2174 | " \n", 2175 | " \n", 2176 | " \n", 2177 | " \n", 2178 | " \n", 2179 | " \n", 2180 | " \n", 2181 | " \n", 2182 | " \n", 2183 | " \n", 2184 | " \n", 2185 | " \n", 2186 | " \n", 2187 | " \n", 2188 | " \n", 2189 | " \n", 2190 | " \n", 2191 | " \n", 2192 | " \n", 2193 | " \n", 2194 | " \n", 2195 | " \n", 2196 | " \n", 2197 | " \n", 2198 | " \n", 2199 | " \n", 2200 | " \n", 2201 | " \n", 2202 | " \n", 2203 | " \n", 2204 | " \n", 2205 | " \n", 2206 | " \n", 2207 | " \n", 2208 | " \n", 2209 | " \n", 2210 | " \n", 2211 | " \n", 2212 | " \n", 2213 | " \n", 2214 | " \n", 2215 | "
AgeCityDegreeGradeNameUnnamed: 0
027TehranMSC83Aliperson1
132EsfahanPHD68Hoseinperson2
223MashhadMA45Sadeqperson3
326TabrizMA72Hamidpesron4
425QazvinMSC54Hamedperson5
530TehranMSC66Rezapeson6
628BabolMSC90Tahaperson7
740TehranPHD81Amirperson8
835ArakPHD76Omidperson9
919MashhadMA38Shayanperson10
\n", 2216 | "
" 2217 | ], 2218 | "text/plain": [ 2219 | " Age City Degree Grade Name Unnamed: 0\n", 2220 | "0 27 Tehran MSC 83 Ali person1\n", 2221 | "1 32 Esfahan PHD 68 Hosein person2\n", 2222 | "2 23 Mashhad MA 45 Sadeq person3\n", 2223 | "3 26 Tabriz MA 72 Hamid pesron4\n", 2224 | "4 25 Qazvin MSC 54 Hamed person5\n", 2225 | "5 30 Tehran MSC 66 Reza peson6\n", 2226 | "6 28 Babol MSC 90 Taha person7\n", 2227 | "7 40 Tehran PHD 81 Amir person8\n", 2228 | "8 35 Arak PHD 76 Omid person9\n", 2229 | "9 19 Mashhad MA 38 Shayan person10" 2230 | ] 2231 | }, 2232 | "execution_count": 36, 2233 | "metadata": {}, 2234 | "output_type": "execute_result" 2235 | } 2236 | ], 2237 | "source": [ 2238 | "# Column sorting.\n", 2239 | "result.sort_index(axis=1)" 2240 | ] 2241 | }, 2242 | { 2243 | "cell_type": "code", 2244 | "execution_count": 37, 2245 | "metadata": {}, 2246 | "outputs": [ 2247 | { 2248 | "data": { 2249 | "text/html": [ 2250 | "
\n", 2251 | "\n", 2264 | "\n", 2265 | " \n", 2266 | " \n", 2267 | " \n", 2268 | " \n", 2269 | " \n", 2270 | " \n", 2271 | " \n", 2272 | " \n", 2273 | " \n", 2274 | " \n", 2275 | " \n", 2276 | " \n", 2277 | " \n", 2278 | " \n", 2279 | " \n", 2280 | " \n", 2281 | " \n", 2282 | " \n", 2283 | " \n", 2284 | " \n", 2285 | " \n", 2286 | " \n", 2287 | " \n", 2288 | " \n", 2289 | " \n", 2290 | " \n", 2291 | " \n", 2292 | " \n", 2293 | " \n", 2294 | " \n", 2295 | " \n", 2296 | " \n", 2297 | " \n", 2298 | " \n", 2299 | " \n", 2300 | " \n", 2301 | " \n", 2302 | " \n", 2303 | " \n", 2304 | " \n", 2305 | " \n", 2306 | " \n", 2307 | " \n", 2308 | " \n", 2309 | " \n", 2310 | " \n", 2311 | " \n", 2312 | " \n", 2313 | " \n", 2314 | " \n", 2315 | " \n", 2316 | " \n", 2317 | " \n", 2318 | " \n", 2319 | " \n", 2320 | " \n", 2321 | " \n", 2322 | " \n", 2323 | " \n", 2324 | " \n", 2325 | " \n", 2326 | " \n", 2327 | " \n", 2328 | " \n", 2329 | " \n", 2330 | " \n", 2331 | " \n", 2332 | " \n", 2333 | " \n", 2334 | " \n", 2335 | " \n", 2336 | " \n", 2337 | " \n", 2338 | " \n", 2339 | " \n", 2340 | " \n", 2341 | " \n", 2342 | " \n", 2343 | " \n", 2344 | " \n", 2345 | " \n", 2346 | " \n", 2347 | " \n", 2348 | " \n", 2349 | " \n", 2350 | " \n", 2351 | " \n", 2352 | " \n", 2353 | " \n", 2354 | " \n", 2355 | " \n", 2356 | " \n", 2357 | " \n", 2358 | " \n", 2359 | " \n", 2360 | " \n", 2361 | " \n", 2362 | " \n", 2363 | " \n", 2364 | " \n", 2365 | " \n", 2366 | " \n", 2367 | " \n", 2368 | "
Unnamed: 0NameAgeCityDegreeGrade
0person1Ali27TehranMSC83
1person2Hosein32EsfahanPHD68
2person3Sadeq23MashhadMA45
3pesron4Hamid26TabrizMA72
4person5Hamed25QazvinMSC54
5peson6Reza30TehranMSC66
6person7Taha28BabolMSC90
7person8Amir40TehranPHD81
8person9Omid35ArakPHD76
9person10Shayan19MashhadMA38
\n", 2369 | "
" 2370 | ], 2371 | "text/plain": [ 2372 | " Unnamed: 0 Name Age City Degree Grade\n", 2373 | "0 person1 Ali 27 Tehran MSC 83\n", 2374 | "1 person2 Hosein 32 Esfahan PHD 68\n", 2375 | "2 person3 Sadeq 23 Mashhad MA 45\n", 2376 | "3 pesron4 Hamid 26 Tabriz MA 72\n", 2377 | "4 person5 Hamed 25 Qazvin MSC 54\n", 2378 | "5 peson6 Reza 30 Tehran MSC 66\n", 2379 | "6 person7 Taha 28 Babol MSC 90\n", 2380 | "7 person8 Amir 40 Tehran PHD 81\n", 2381 | "8 person9 Omid 35 Arak PHD 76\n", 2382 | "9 person10 Shayan 19 Mashhad MA 38" 2383 | ] 2384 | }, 2385 | "execution_count": 37, 2386 | "metadata": {}, 2387 | "output_type": "execute_result" 2388 | } 2389 | ], 2390 | "source": [ 2391 | "# Row sorting.\n", 2392 | "result.sort_index(axis=0)" 2393 | ] 2394 | }, 2395 | { 2396 | "cell_type": "code", 2397 | "execution_count": 38, 2398 | "metadata": {}, 2399 | "outputs": [], 2400 | "source": [ 2401 | "# Remove duplicate values of a column.\n", 2402 | "result.sort_values(\"Degree\", inplace=True)" 2403 | ] 2404 | }, 2405 | { 2406 | "cell_type": "code", 2407 | "execution_count": 39, 2408 | "metadata": {}, 2409 | "outputs": [ 2410 | { 2411 | "data": { 2412 | "text/html": [ 2413 | "
\n", 2414 | "\n", 2427 | "\n", 2428 | " \n", 2429 | " \n", 2430 | " \n", 2431 | " \n", 2432 | " \n", 2433 | " \n", 2434 | " \n", 2435 | " \n", 2436 | " \n", 2437 | " \n", 2438 | " \n", 2439 | " \n", 2440 | " \n", 2441 | " \n", 2442 | " \n", 2443 | " \n", 2444 | " \n", 2445 | " \n", 2446 | " \n", 2447 | " \n", 2448 | " \n", 2449 | " \n", 2450 | " \n", 2451 | " \n", 2452 | " \n", 2453 | " \n", 2454 | " \n", 2455 | " \n", 2456 | " \n", 2457 | " \n", 2458 | " \n", 2459 | " \n", 2460 | " \n", 2461 | " \n", 2462 | " \n", 2463 | " \n", 2464 | " \n", 2465 | " \n", 2466 | " \n", 2467 | " \n", 2468 | " \n", 2469 | " \n", 2470 | " \n", 2471 | " \n", 2472 | " \n", 2473 | " \n", 2474 | " \n", 2475 | " \n", 2476 | " \n", 2477 | " \n", 2478 | " \n", 2479 | " \n", 2480 | " \n", 2481 | " \n", 2482 | " \n", 2483 | " \n", 2484 | " \n", 2485 | " \n", 2486 | " \n", 2487 | " \n", 2488 | " \n", 2489 | " \n", 2490 | " \n", 2491 | " \n", 2492 | " \n", 2493 | " \n", 2494 | " \n", 2495 | " \n", 2496 | " \n", 2497 | " \n", 2498 | " \n", 2499 | " \n", 2500 | " \n", 2501 | " \n", 2502 | " \n", 2503 | " \n", 2504 | " \n", 2505 | " \n", 2506 | " \n", 2507 | " \n", 2508 | " \n", 2509 | " \n", 2510 | " \n", 2511 | " \n", 2512 | " \n", 2513 | " \n", 2514 | " \n", 2515 | " \n", 2516 | " \n", 2517 | " \n", 2518 | " \n", 2519 | " \n", 2520 | " \n", 2521 | " \n", 2522 | " \n", 2523 | " \n", 2524 | " \n", 2525 | " \n", 2526 | " \n", 2527 | " \n", 2528 | " \n", 2529 | " \n", 2530 | " \n", 2531 | "
Unnamed: 0NameAgeCityDegreeGrade
2person3Sadeq23MashhadMA45
3pesron4Hamid26TabrizMA72
9person10Shayan19MashhadMA38
0person1Ali27TehranMSC83
4person5Hamed25QazvinMSC54
5peson6Reza30TehranMSC66
6person7Taha28BabolMSC90
1person2Hosein32EsfahanPHD68
7person8Amir40TehranPHD81
8person9Omid35ArakPHD76
\n", 2532 | "
" 2533 | ], 2534 | "text/plain": [ 2535 | " Unnamed: 0 Name Age City Degree Grade\n", 2536 | "2 person3 Sadeq 23 Mashhad MA 45\n", 2537 | "3 pesron4 Hamid 26 Tabriz MA 72\n", 2538 | "9 person10 Shayan 19 Mashhad MA 38\n", 2539 | "0 person1 Ali 27 Tehran MSC 83\n", 2540 | "4 person5 Hamed 25 Qazvin MSC 54\n", 2541 | "5 peson6 Reza 30 Tehran MSC 66\n", 2542 | "6 person7 Taha 28 Babol MSC 90\n", 2543 | "1 person2 Hosein 32 Esfahan PHD 68\n", 2544 | "7 person8 Amir 40 Tehran PHD 81\n", 2545 | "8 person9 Omid 35 Arak PHD 76" 2546 | ] 2547 | }, 2548 | "execution_count": 39, 2549 | "metadata": {}, 2550 | "output_type": "execute_result" 2551 | } 2552 | ], 2553 | "source": [ 2554 | "# Display even duplicate values of a column.\n", 2555 | "result.sort_values(\"Degree\", inplace=False)" 2556 | ] 2557 | }, 2558 | { 2559 | "cell_type": "code", 2560 | "execution_count": 40, 2561 | "metadata": {}, 2562 | "outputs": [ 2563 | { 2564 | "data": { 2565 | "text/html": [ 2566 | "
\n", 2567 | "\n", 2580 | "\n", 2581 | " \n", 2582 | " \n", 2583 | " \n", 2584 | " \n", 2585 | " \n", 2586 | " \n", 2587 | " \n", 2588 | " \n", 2589 | " \n", 2590 | " \n", 2591 | " \n", 2592 | " \n", 2593 | " \n", 2594 | " \n", 2595 | " \n", 2596 | " \n", 2597 | " \n", 2598 | " \n", 2599 | " \n", 2600 | " \n", 2601 | " \n", 2602 | " \n", 2603 | " \n", 2604 | " \n", 2605 | " \n", 2606 | " \n", 2607 | " \n", 2608 | " \n", 2609 | " \n", 2610 | " \n", 2611 | " \n", 2612 | " \n", 2613 | " \n", 2614 | " \n", 2615 | " \n", 2616 | " \n", 2617 | " \n", 2618 | " \n", 2619 | " \n", 2620 | " \n", 2621 | " \n", 2622 | " \n", 2623 | " \n", 2624 | " \n", 2625 | " \n", 2626 | " \n", 2627 | " \n", 2628 | " \n", 2629 | " \n", 2630 | " \n", 2631 | " \n", 2632 | " \n", 2633 | " \n", 2634 | " \n", 2635 | " \n", 2636 | " \n", 2637 | " \n", 2638 | " \n", 2639 | " \n", 2640 | " \n", 2641 | " \n", 2642 | " \n", 2643 | " \n", 2644 | " \n", 2645 | " \n", 2646 | " \n", 2647 | " \n", 2648 | " \n", 2649 | " \n", 2650 | " \n", 2651 | " \n", 2652 | " \n", 2653 | " \n", 2654 | " \n", 2655 | " \n", 2656 | " \n", 2657 | " \n", 2658 | " \n", 2659 | " \n", 2660 | " \n", 2661 | " \n", 2662 | " \n", 2663 | " \n", 2664 | " \n", 2665 | " \n", 2666 | " \n", 2667 | " \n", 2668 | " \n", 2669 | " \n", 2670 | " \n", 2671 | " \n", 2672 | " \n", 2673 | " \n", 2674 | " \n", 2675 | " \n", 2676 | " \n", 2677 | " \n", 2678 | " \n", 2679 | " \n", 2680 | " \n", 2681 | " \n", 2682 | " \n", 2683 | " \n", 2684 | "
Unnamed: 0NameAgeCityDegreeGrade
9person10Shayan19MashhadMA38
2person3Sadeq23MashhadMA45
4person5Hamed25QazvinMSC54
3pesron4Hamid26TabrizMA72
0person1Ali27TehranMSC83
6person7Taha28BabolMSC90
5peson6Reza30TehranMSC66
1person2Hosein32EsfahanPHD68
8person9Omid35ArakPHD76
7person8Amir40TehranPHD81
\n", 2685 | "
" 2686 | ], 2687 | "text/plain": [ 2688 | " Unnamed: 0 Name Age City Degree Grade\n", 2689 | "9 person10 Shayan 19 Mashhad MA 38\n", 2690 | "2 person3 Sadeq 23 Mashhad MA 45\n", 2691 | "4 person5 Hamed 25 Qazvin MSC 54\n", 2692 | "3 pesron4 Hamid 26 Tabriz MA 72\n", 2693 | "0 person1 Ali 27 Tehran MSC 83\n", 2694 | "6 person7 Taha 28 Babol MSC 90\n", 2695 | "5 peson6 Reza 30 Tehran MSC 66\n", 2696 | "1 person2 Hosein 32 Esfahan PHD 68\n", 2697 | "8 person9 Omid 35 Arak PHD 76\n", 2698 | "7 person8 Amir 40 Tehran PHD 81" 2699 | ] 2700 | }, 2701 | "execution_count": 40, 2702 | "metadata": {}, 2703 | "output_type": "execute_result" 2704 | } 2705 | ], 2706 | "source": [ 2707 | "# Descending sort of a column.\n", 2708 | "result.sort_values(\"Age\", ascending=True)" 2709 | ] 2710 | }, 2711 | { 2712 | "cell_type": "code", 2713 | "execution_count": 41, 2714 | "metadata": {}, 2715 | "outputs": [ 2716 | { 2717 | "data": { 2718 | "text/html": [ 2719 | "
\n", 2720 | "\n", 2733 | "\n", 2734 | " \n", 2735 | " \n", 2736 | " \n", 2737 | " \n", 2738 | " \n", 2739 | " \n", 2740 | " \n", 2741 | " \n", 2742 | " \n", 2743 | " \n", 2744 | " \n", 2745 | " \n", 2746 | " \n", 2747 | " \n", 2748 | " \n", 2749 | " \n", 2750 | " \n", 2751 | " \n", 2752 | " \n", 2753 | " \n", 2754 | " \n", 2755 | " \n", 2756 | " \n", 2757 | " \n", 2758 | " \n", 2759 | " \n", 2760 | " \n", 2761 | " \n", 2762 | " \n", 2763 | " \n", 2764 | " \n", 2765 | " \n", 2766 | " \n", 2767 | " \n", 2768 | " \n", 2769 | " \n", 2770 | " \n", 2771 | " \n", 2772 | " \n", 2773 | " \n", 2774 | " \n", 2775 | " \n", 2776 | " \n", 2777 | " \n", 2778 | " \n", 2779 | " \n", 2780 | " \n", 2781 | " \n", 2782 | " \n", 2783 | " \n", 2784 | " \n", 2785 | " \n", 2786 | " \n", 2787 | " \n", 2788 | " \n", 2789 | " \n", 2790 | " \n", 2791 | " \n", 2792 | " \n", 2793 | " \n", 2794 | " \n", 2795 | " \n", 2796 | " \n", 2797 | " \n", 2798 | " \n", 2799 | " \n", 2800 | " \n", 2801 | " \n", 2802 | " \n", 2803 | " \n", 2804 | " \n", 2805 | " \n", 2806 | " \n", 2807 | " \n", 2808 | " \n", 2809 | " \n", 2810 | " \n", 2811 | " \n", 2812 | " \n", 2813 | " \n", 2814 | " \n", 2815 | " \n", 2816 | " \n", 2817 | " \n", 2818 | " \n", 2819 | " \n", 2820 | " \n", 2821 | " \n", 2822 | " \n", 2823 | " \n", 2824 | " \n", 2825 | " \n", 2826 | " \n", 2827 | " \n", 2828 | " \n", 2829 | " \n", 2830 | " \n", 2831 | " \n", 2832 | " \n", 2833 | " \n", 2834 | " \n", 2835 | " \n", 2836 | " \n", 2837 | "
Unnamed: 0NameAgeCityDegreeGrade
7person8Amir40TehranPHD81
8person9Omid35ArakPHD76
1person2Hosein32EsfahanPHD68
5peson6Reza30TehranMSC66
6person7Taha28BabolMSC90
0person1Ali27TehranMSC83
3pesron4Hamid26TabrizMA72
4person5Hamed25QazvinMSC54
2person3Sadeq23MashhadMA45
9person10Shayan19MashhadMA38
\n", 2838 | "
" 2839 | ], 2840 | "text/plain": [ 2841 | " Unnamed: 0 Name Age City Degree Grade\n", 2842 | "7 person8 Amir 40 Tehran PHD 81\n", 2843 | "8 person9 Omid 35 Arak PHD 76\n", 2844 | "1 person2 Hosein 32 Esfahan PHD 68\n", 2845 | "5 peson6 Reza 30 Tehran MSC 66\n", 2846 | "6 person7 Taha 28 Babol MSC 90\n", 2847 | "0 person1 Ali 27 Tehran MSC 83\n", 2848 | "3 pesron4 Hamid 26 Tabriz MA 72\n", 2849 | "4 person5 Hamed 25 Qazvin MSC 54\n", 2850 | "2 person3 Sadeq 23 Mashhad MA 45\n", 2851 | "9 person10 Shayan 19 Mashhad MA 38" 2852 | ] 2853 | }, 2854 | "execution_count": 41, 2855 | "metadata": {}, 2856 | "output_type": "execute_result" 2857 | } 2858 | ], 2859 | "source": [ 2860 | "# Ascending sort of a column.\n", 2861 | "result.sort_values(\"Age\", ascending=False)" 2862 | ] 2863 | }, 2864 | { 2865 | "cell_type": "code", 2866 | "execution_count": null, 2867 | "metadata": {}, 2868 | "outputs": [], 2869 | "source": [] 2870 | }, 2871 | { 2872 | "cell_type": "markdown", 2873 | "metadata": {}, 2874 | "source": [ 2875 | "# Using the graph for our values." 2876 | ] 2877 | }, 2878 | { 2879 | "cell_type": "markdown", 2880 | "metadata": {}, 2881 | "source": [ 2882 | "### Install matplotlib" 2883 | ] 2884 | }, 2885 | { 2886 | "cell_type": "code", 2887 | "execution_count": 42, 2888 | "metadata": {}, 2889 | "outputs": [ 2890 | { 2891 | "name": "stdout", 2892 | "output_type": "stream", 2893 | "text": [ 2894 | "Requirement already satisfied: matplotlib in c:\\users\\user\\anaconda3\\lib\\site-packages (3.3.2)\n", 2895 | "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib) (2.4.7)\n", 2896 | "Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib) (0.10.0)\n", 2897 | "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib) (2022.6.15)\n", 2898 | "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib) (8.0.1)\n", 2899 | "Requirement already satisfied: numpy>=1.15 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib) (1.24.3)\n", 2900 | "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.1)\n", 2901 | "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib) (1.3.0)\n", 2902 | "Requirement already satisfied: six in c:\\users\\user\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n" 2903 | ] 2904 | } 2905 | ], 2906 | "source": [ 2907 | "!pip install matplotlib" 2908 | ] 2909 | }, 2910 | { 2911 | "cell_type": "code", 2912 | "execution_count": 43, 2913 | "metadata": {}, 2914 | "outputs": [], 2915 | "source": [ 2916 | "import matplotlib.pyplot as plt" 2917 | ] 2918 | }, 2919 | { 2920 | "cell_type": "code", 2921 | "execution_count": 44, 2922 | "metadata": {}, 2923 | "outputs": [ 2924 | { 2925 | "data": { 2926 | "image/png": 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\n", 2927 | "text/plain": [ 2928 | "
" 2929 | ] 2930 | }, 2931 | "metadata": { 2932 | "needs_background": "light" 2933 | }, 2934 | "output_type": "display_data" 2935 | } 2936 | ], 2937 | "source": [ 2938 | "result = pd.read_csv(\"Data.csv\")\n", 2939 | "result[\"Age\"].plot()\n", 2940 | "plt.show()" 2941 | ] 2942 | }, 2943 | { 2944 | "cell_type": "code", 2945 | "execution_count": null, 2946 | "metadata": {}, 2947 | "outputs": [], 2948 | "source": [] 2949 | }, 2950 | { 2951 | "cell_type": "code", 2952 | "execution_count": 45, 2953 | "metadata": {}, 2954 | "outputs": [], 2955 | "source": [ 2956 | "#My datas\n", 2957 | "data1 = {\n", 2958 | " \"Name\":[\"ashkan\",\"ali\",\"mamad\"],\n", 2959 | " \"Score\":[100,39,55],\n", 2960 | " \"Quality\":[\"High\",\"Low\",\"Mid\"]\n", 2961 | "}\n", 2962 | "data2 = {\n", 2963 | " \"Name\":[\"ashkan\",\"ali\",\"mamad\"],\n", 2964 | " \"Score\":[95,70,22],\n", 2965 | " \"Quality\":[\"High\",\"Mid\",\"Low\"]\n", 2966 | "}\n", 2967 | "\n", 2968 | "result1 = pd.DataFrame(data1)\n", 2969 | "result2 = pd.DataFrame(data2)" 2970 | ] 2971 | }, 2972 | { 2973 | "cell_type": "code", 2974 | "execution_count": 46, 2975 | "metadata": {}, 2976 | "outputs": [ 2977 | { 2978 | "data": { 2979 | "text/html": [ 2980 | "
\n", 2981 | "\n", 2994 | "\n", 2995 | " \n", 2996 | " \n", 2997 | " \n", 2998 | " \n", 2999 | " \n", 3000 | " \n", 3001 | " \n", 3002 | " \n", 3003 | " \n", 3004 | " \n", 3005 | " \n", 3006 | " \n", 3007 | " \n", 3008 | " \n", 3009 | " \n", 3010 | " \n", 3011 | " \n", 3012 | " \n", 3013 | " \n", 3014 | " \n", 3015 | " \n", 3016 | " \n", 3017 | " \n", 3018 | " \n", 3019 | " \n", 3020 | " \n", 3021 | " \n", 3022 | " \n", 3023 | " \n", 3024 | " \n", 3025 | " \n", 3026 | " \n", 3027 | " \n", 3028 | " \n", 3029 | " \n", 3030 | " \n", 3031 | "
NameScore_xQuality_xScore_yQuality_y
0ashkan100High95High
1ali39Low70Mid
2mamad55Mid22Low
\n", 3032 | "
" 3033 | ], 3034 | "text/plain": [ 3035 | " Name Score_x Quality_x Score_y Quality_y\n", 3036 | "0 ashkan 100 High 95 High\n", 3037 | "1 ali 39 Low 70 Mid\n", 3038 | "2 mamad 55 Mid 22 Low" 3039 | ] 3040 | }, 3041 | "execution_count": 46, 3042 | "metadata": {}, 3043 | "output_type": "execute_result" 3044 | } 3045 | ], 3046 | "source": [ 3047 | "# Revision of additional values.\n", 3048 | "pd.merge(result1, result2, on=\"Name\")" 3049 | ] 3050 | }, 3051 | { 3052 | "cell_type": "code", 3053 | "execution_count": 47, 3054 | "metadata": {}, 3055 | "outputs": [ 3056 | { 3057 | "data": { 3058 | "text/html": [ 3059 | "
\n", 3060 | "\n", 3073 | "\n", 3074 | " \n", 3075 | " \n", 3076 | " \n", 3077 | " \n", 3078 | " \n", 3079 | " \n", 3080 | " \n", 3081 | " \n", 3082 | " \n", 3083 | " \n", 3084 | " \n", 3085 | " \n", 3086 | " \n", 3087 | " \n", 3088 | " \n", 3089 | " \n", 3090 | " \n", 3091 | " \n", 3092 | " \n", 3093 | " \n", 3094 | " \n", 3095 | " \n", 3096 | " \n", 3097 | " \n", 3098 | " \n", 3099 | " \n", 3100 | " \n", 3101 | " \n", 3102 | " \n", 3103 | " \n", 3104 | " \n", 3105 | " \n", 3106 | " \n", 3107 | " \n", 3108 | " \n", 3109 | " \n", 3110 | " \n", 3111 | " \n", 3112 | " \n", 3113 | "
Score_leftQuality_leftScoreQuality
Name
ashkan100High95High
ali39Low70Mid
mamad55Mid22Low
\n", 3114 | "
" 3115 | ], 3116 | "text/plain": [ 3117 | " Score_left Quality_left Score Quality\n", 3118 | "Name \n", 3119 | "ashkan 100 High 95 High\n", 3120 | "ali 39 Low 70 Mid\n", 3121 | "mamad 55 Mid 22 Low" 3122 | ] 3123 | }, 3124 | "execution_count": 47, 3125 | "metadata": {}, 3126 | "output_type": "execute_result" 3127 | } 3128 | ], 3129 | "source": [ 3130 | "# Connect two data.\n", 3131 | "result1.set_index(\"Name\", inplace=True)\n", 3132 | "result2.set_index(\"Name\", inplace=True)\n", 3133 | "\n", 3134 | "result1.join(result2, lsuffix=\"_left\")" 3135 | ] 3136 | }, 3137 | { 3138 | "cell_type": "code", 3139 | "execution_count": 48, 3140 | "metadata": {}, 3141 | "outputs": [ 3142 | { 3143 | "name": "stdout", 3144 | "output_type": "stream", 3145 | "text": [ 3146 | " Name Score_x Quality_x Score_y Quality_y\n", 3147 | "0 ashkan 100 High 95 High\n" 3148 | ] 3149 | } 3150 | ], 3151 | "source": [ 3152 | "# Find identical values of a column.\n", 3153 | "\n", 3154 | "#My datas\n", 3155 | "data1 = {\n", 3156 | " \"Name\":[\"ashkan\",\"majid\",\"sara\"],\n", 3157 | " \"Score\":[100,39,55],\n", 3158 | " \"Quality\":[\"High\",\"Low\",\"Mid\"]\n", 3159 | "}\n", 3160 | "data2 = {\n", 3161 | " \"Name\":[\"ashkan\",\"ali\",\"mamad\"],\n", 3162 | " \"Score\":[95,70,22],\n", 3163 | " \"Quality\":[\"High\",\"Mid\",\"Low\"]\n", 3164 | "}\n", 3165 | "\n", 3166 | "result1 = pd.DataFrame(data1)\n", 3167 | "result2 = pd.DataFrame(data2)\n", 3168 | "\n", 3169 | "final = pd.merge(result1, result2, on=\"Name\")\n", 3170 | "print(final)" 3171 | ] 3172 | }, 3173 | { 3174 | "cell_type": "code", 3175 | "execution_count": 49, 3176 | "metadata": {}, 3177 | "outputs": [], 3178 | "source": [ 3179 | "# Participating in two data or not participating in two data." 3180 | ] 3181 | }, 3182 | { 3183 | "cell_type": "code", 3184 | "execution_count": 50, 3185 | "metadata": {}, 3186 | "outputs": [ 3187 | { 3188 | "data": { 3189 | "text/html": [ 3190 | "
\n", 3191 | "\n", 3204 | "\n", 3205 | " \n", 3206 | " \n", 3207 | " \n", 3208 | " \n", 3209 | " \n", 3210 | " \n", 3211 | " \n", 3212 | " \n", 3213 | " \n", 3214 | " \n", 3215 | " \n", 3216 | " \n", 3217 | " \n", 3218 | " \n", 3219 | " \n", 3220 | " \n", 3221 | " \n", 3222 | " \n", 3223 | " \n", 3224 | " \n", 3225 | " \n", 3226 | " \n", 3227 | " \n", 3228 | " \n", 3229 | " \n", 3230 | " \n", 3231 | " \n", 3232 | " \n", 3233 | " \n", 3234 | " \n", 3235 | " \n", 3236 | " \n", 3237 | " \n", 3238 | " \n", 3239 | " \n", 3240 | " \n", 3241 | "
NameScore_xQuality_xScore_yQuality_y
0ashkan100High95.0High
1majid39LowNaNNaN
2sara55MidNaNNaN
\n", 3242 | "
" 3243 | ], 3244 | "text/plain": [ 3245 | " Name Score_x Quality_x Score_y Quality_y\n", 3246 | "0 ashkan 100 High 95.0 High\n", 3247 | "1 majid 39 Low NaN NaN\n", 3248 | "2 sara 55 Mid NaN NaN" 3249 | ] 3250 | }, 3251 | "execution_count": 50, 3252 | "metadata": {}, 3253 | "output_type": "execute_result" 3254 | } 3255 | ], 3256 | "source": [ 3257 | "#left\n", 3258 | "final = pd.merge(result1, result2, on=\"Name\", how=\"left\")\n", 3259 | "final" 3260 | ] 3261 | }, 3262 | { 3263 | "cell_type": "code", 3264 | "execution_count": 51, 3265 | "metadata": {}, 3266 | "outputs": [ 3267 | { 3268 | "data": { 3269 | "text/html": [ 3270 | "
\n", 3271 | "\n", 3284 | "\n", 3285 | " \n", 3286 | " \n", 3287 | " \n", 3288 | " \n", 3289 | " \n", 3290 | " \n", 3291 | " \n", 3292 | " \n", 3293 | " \n", 3294 | " \n", 3295 | " \n", 3296 | " \n", 3297 | " \n", 3298 | " \n", 3299 | " \n", 3300 | " \n", 3301 | " \n", 3302 | " \n", 3303 | " \n", 3304 | " \n", 3305 | " \n", 3306 | " \n", 3307 | " \n", 3308 | " \n", 3309 | " \n", 3310 | " \n", 3311 | " \n", 3312 | " \n", 3313 | " \n", 3314 | " \n", 3315 | " \n", 3316 | " \n", 3317 | " \n", 3318 | " \n", 3319 | " \n", 3320 | " \n", 3321 | "
NameScore_xQuality_xScore_yQuality_y
0ashkan100.0High95High
1aliNaNNaN70Mid
2mamadNaNNaN22Low
\n", 3322 | "
" 3323 | ], 3324 | "text/plain": [ 3325 | " Name Score_x Quality_x Score_y Quality_y\n", 3326 | "0 ashkan 100.0 High 95 High\n", 3327 | "1 ali NaN NaN 70 Mid\n", 3328 | "2 mamad NaN NaN 22 Low" 3329 | ] 3330 | }, 3331 | "execution_count": 51, 3332 | "metadata": {}, 3333 | "output_type": "execute_result" 3334 | } 3335 | ], 3336 | "source": [ 3337 | "#right\n", 3338 | "final = pd.merge(result1, result2, on=\"Name\", how=\"right\")\n", 3339 | "final" 3340 | ] 3341 | }, 3342 | { 3343 | "cell_type": "code", 3344 | "execution_count": 52, 3345 | "metadata": {}, 3346 | "outputs": [ 3347 | { 3348 | "data": { 3349 | "text/html": [ 3350 | "
\n", 3351 | "\n", 3364 | "\n", 3365 | " \n", 3366 | " \n", 3367 | " \n", 3368 | " \n", 3369 | " \n", 3370 | " \n", 3371 | " \n", 3372 | " \n", 3373 | " \n", 3374 | " \n", 3375 | " \n", 3376 | " \n", 3377 | " \n", 3378 | " \n", 3379 | " \n", 3380 | " \n", 3381 | " \n", 3382 | " \n", 3383 | " \n", 3384 | " \n", 3385 | " \n", 3386 | " \n", 3387 | " \n", 3388 | " \n", 3389 | " \n", 3390 | " \n", 3391 | " \n", 3392 | " \n", 3393 | " \n", 3394 | " \n", 3395 | " \n", 3396 | " \n", 3397 | " \n", 3398 | " \n", 3399 | " \n", 3400 | " \n", 3401 | " \n", 3402 | " \n", 3403 | " \n", 3404 | " \n", 3405 | " \n", 3406 | " \n", 3407 | " \n", 3408 | " \n", 3409 | " \n", 3410 | " \n", 3411 | " \n", 3412 | " \n", 3413 | " \n", 3414 | " \n", 3415 | " \n", 3416 | " \n", 3417 | "
NameScore_xQuality_xScore_yQuality_y
0ashkan100.0High95.0High
1majid39.0LowNaNNaN
2sara55.0MidNaNNaN
3aliNaNNaN70.0Mid
4mamadNaNNaN22.0Low
\n", 3418 | "
" 3419 | ], 3420 | "text/plain": [ 3421 | " Name Score_x Quality_x Score_y Quality_y\n", 3422 | "0 ashkan 100.0 High 95.0 High\n", 3423 | "1 majid 39.0 Low NaN NaN\n", 3424 | "2 sara 55.0 Mid NaN NaN\n", 3425 | "3 ali NaN NaN 70.0 Mid\n", 3426 | "4 mamad NaN NaN 22.0 Low" 3427 | ] 3428 | }, 3429 | "execution_count": 52, 3430 | "metadata": {}, 3431 | "output_type": "execute_result" 3432 | } 3433 | ], 3434 | "source": [ 3435 | "# Total data\n", 3436 | "final = pd.merge(result1, result2, on=\"Name\", how=\"outer\")\n", 3437 | "final" 3438 | ] 3439 | }, 3440 | { 3441 | "cell_type": "code", 3442 | "execution_count": 53, 3443 | "metadata": {}, 3444 | "outputs": [ 3445 | { 3446 | "data": { 3447 | "text/html": [ 3448 | "
\n", 3449 | "\n", 3462 | "\n", 3463 | " \n", 3464 | " \n", 3465 | " \n", 3466 | " \n", 3467 | " \n", 3468 | " \n", 3469 | " \n", 3470 | " \n", 3471 | " \n", 3472 | " \n", 3473 | " \n", 3474 | " \n", 3475 | " \n", 3476 | " \n", 3477 | " \n", 3478 | " \n", 3479 | " \n", 3480 | " \n", 3481 | " \n", 3482 | " \n", 3483 | " \n", 3484 | " \n", 3485 | " \n", 3486 | " \n", 3487 | " \n", 3488 | " \n", 3489 | " \n", 3490 | " \n", 3491 | " \n", 3492 | " \n", 3493 | " \n", 3494 | " \n", 3495 | " \n", 3496 | " \n", 3497 | " \n", 3498 | " \n", 3499 | " \n", 3500 | " \n", 3501 | " \n", 3502 | " \n", 3503 | " \n", 3504 | " \n", 3505 | " \n", 3506 | " \n", 3507 | " \n", 3508 | " \n", 3509 | "
NameScoreQuality
0ashkan100High
1majid39Low
2sara55Mid
0ashkan95High
1ali70Mid
2mamad22Low
\n", 3510 | "
" 3511 | ], 3512 | "text/plain": [ 3513 | " Name Score Quality\n", 3514 | "0 ashkan 100 High\n", 3515 | "1 majid 39 Low\n", 3516 | "2 sara 55 Mid\n", 3517 | "0 ashkan 95 High\n", 3518 | "1 ali 70 Mid\n", 3519 | "2 mamad 22 Low" 3520 | ] 3521 | }, 3522 | "execution_count": 53, 3523 | "metadata": {}, 3524 | "output_type": "execute_result" 3525 | } 3526 | ], 3527 | "source": [ 3528 | "# Concatenate two data\n", 3529 | "final = pd.concat([result1, result2])\n", 3530 | "final" 3531 | ] 3532 | }, 3533 | { 3534 | "cell_type": "code", 3535 | "execution_count": null, 3536 | "metadata": {}, 3537 | "outputs": [], 3538 | "source": [] 3539 | }, 3540 | { 3541 | "cell_type": "code", 3542 | "execution_count": 54, 3543 | "metadata": {}, 3544 | "outputs": [ 3545 | { 3546 | "data": { 3547 | "text/plain": [ 3548 | "{'ashkan': [0], 'majid': [1], 'sara': [2]}" 3549 | ] 3550 | }, 3551 | "execution_count": 54, 3552 | "metadata": {}, 3553 | "output_type": "execute_result" 3554 | } 3555 | ], 3556 | "source": [ 3557 | "# It says in which lines that value exists.\n", 3558 | "\n", 3559 | "#My datas\n", 3560 | "data = {\n", 3561 | " \"Name\":[\"ashkan\",\"majid\",\"sara\"],\n", 3562 | " \"Score\":[100,39,55],\n", 3563 | " \"Quality\":[\"High\",\"Low\",\"Mid\"]\n", 3564 | "}\n", 3565 | "\n", 3566 | "result = pd.DataFrame(data)\n", 3567 | "\n", 3568 | "group = result.groupby(\"Name\")\n", 3569 | "group.groups" 3570 | ] 3571 | }, 3572 | { 3573 | "cell_type": "code", 3574 | "execution_count": 55, 3575 | "metadata": {}, 3576 | "outputs": [ 3577 | { 3578 | "name": "stdout", 3579 | "output_type": "stream", 3580 | "text": [ 3581 | " Name Score Quality\n", 3582 | "0 ashkan 100 High\n", 3583 | "1 majid 39 Low\n", 3584 | "2 sara 55 Mid \n", 3585 | "\n" 3586 | ] 3587 | }, 3588 | { 3589 | "data": { 3590 | "text/plain": [ 3591 | "{False: [1, 2], True: [0]}" 3592 | ] 3593 | }, 3594 | "execution_count": 55, 3595 | "metadata": {}, 3596 | "output_type": "execute_result" 3597 | } 3598 | ], 3599 | "source": [ 3600 | "# Compare columns based on true or false.\n", 3601 | "# We are looking for the name column that has the name \"ashkan\" in it\n", 3602 | "\n", 3603 | "print(result, \"\\n\")\n", 3604 | "\n", 3605 | "def checker(name):\n", 3606 | " if result.loc[name].Name == \"ashkan\":\n", 3607 | " return True\n", 3608 | " else:\n", 3609 | " return False\n", 3610 | "\n", 3611 | "result.groupby(checker).groups" 3612 | ] 3613 | }, 3614 | { 3615 | "cell_type": "code", 3616 | "execution_count": 56, 3617 | "metadata": {}, 3618 | "outputs": [ 3619 | { 3620 | "data": { 3621 | "text/html": [ 3622 | "
\n", 3623 | "\n", 3636 | "\n", 3637 | " \n", 3638 | " \n", 3639 | " \n", 3640 | " \n", 3641 | " \n", 3642 | " \n", 3643 | " \n", 3644 | " \n", 3645 | " \n", 3646 | " \n", 3647 | " \n", 3648 | " \n", 3649 | " \n", 3650 | " \n", 3651 | " \n", 3652 | " \n", 3653 | " \n", 3654 | " \n", 3655 | " \n", 3656 | " \n", 3657 | " \n", 3658 | " \n", 3659 | " \n", 3660 | " \n", 3661 | "
Score
Name
ashkan274
majid143
sara140
\n", 3662 | "
" 3663 | ], 3664 | "text/plain": [ 3665 | " Score\n", 3666 | "Name \n", 3667 | "ashkan 274\n", 3668 | "majid 143\n", 3669 | "sara 140" 3670 | ] 3671 | }, 3672 | "execution_count": 56, 3673 | "metadata": {}, 3674 | "output_type": "execute_result" 3675 | } 3676 | ], 3677 | "source": [ 3678 | "# Apply the function on each of our cells.\n", 3679 | "\n", 3680 | "# My data\n", 3681 | "data = {\n", 3682 | " \"Name\":[\"ashkan\", \"ashkan\", \"ashkan\", \"majid\", \"majid\",\"majid\", \"sara\", \"sara\"],\n", 3683 | " \"Score\":[100, 79, 95, 99, 12, 32 ,52, 88],\n", 3684 | " \"Quality\":[\"High\", \"Low\", \"Mid\", \"Low\", \"High\", \"Low\", \"Mid\", \"Low\"]\n", 3685 | "}\n", 3686 | "\n", 3687 | "result = pd.DataFrame(data)\n", 3688 | "\n", 3689 | "group = result.groupby(\"Name\")\n", 3690 | "\n", 3691 | "group.agg(sum)" 3692 | ] 3693 | }, 3694 | { 3695 | "cell_type": "code", 3696 | "execution_count": 57, 3697 | "metadata": {}, 3698 | "outputs": [ 3699 | { 3700 | "name": "stdout", 3701 | "output_type": "stream", 3702 | "text": [ 3703 | "This is for ashkan\n", 3704 | " Name Score Quality\n", 3705 | "0 ashkan 100 High\n", 3706 | "1 ashkan 79 Low\n", 3707 | "2 ashkan 95 Mid \n", 3708 | "\n", 3709 | "This is for majid\n", 3710 | " Name Score Quality\n", 3711 | "3 majid 99 Low\n", 3712 | "4 majid 12 High\n", 3713 | "5 majid 32 Low \n", 3714 | "\n", 3715 | "This is for sara\n", 3716 | " Name Score Quality\n", 3717 | "6 sara 52 Mid\n", 3718 | "7 sara 88 Low \n", 3719 | "\n" 3720 | ] 3721 | } 3722 | ], 3723 | "source": [ 3724 | "# to split\n", 3725 | "for name, data in result.groupby(\"Name\"):\n", 3726 | " print(f\"This is for {name}\")\n", 3727 | " print(data, \"\\n\")" 3728 | ] 3729 | }, 3730 | { 3731 | "cell_type": "code", 3732 | "execution_count": 58, 3733 | "metadata": {}, 3734 | "outputs": [ 3735 | { 3736 | "data": { 3737 | "text/plain": [ 3738 | "Name object\n", 3739 | "Score int64\n", 3740 | "Quality object\n", 3741 | "dtype: object" 3742 | ] 3743 | }, 3744 | "execution_count": 58, 3745 | "metadata": {}, 3746 | "output_type": "execute_result" 3747 | } 3748 | ], 3749 | "source": [ 3750 | "# View the type of each column.\n", 3751 | "result.dtypes" 3752 | ] 3753 | }, 3754 | { 3755 | "cell_type": "code", 3756 | "execution_count": null, 3757 | "metadata": {}, 3758 | "outputs": [], 3759 | "source": [] 3760 | }, 3761 | { 3762 | "cell_type": "code", 3763 | "execution_count": 59, 3764 | "metadata": {}, 3765 | "outputs": [ 3766 | { 3767 | "name": "stdout", 3768 | "output_type": "stream", 3769 | "text": [ 3770 | "Date 2012-02-02\n", 3771 | "Consumption 1563.41\n", 3772 | "Wind 73.469\n", 3773 | "Solar 44.675\n", 3774 | "Wind+Solar 118.144\n", 3775 | "Name: 2012-02-02 00:00:00, dtype: object \n", 3776 | "\n", 3777 | " Date Consumption Wind Solar Wind+Solar\n", 3778 | "2017-10-31 2017-10-31 1204.08577 256.960 56.767 313.727\n", 3779 | "2017-11-01 2017-11-01 1309.18478 420.446 37.046 457.492\n", 3780 | "2017-11-02 2017-11-02 1474.19484 336.745 51.504 388.249\n", 3781 | "2017-11-03 2017-11-03 1471.18241 115.927 50.761 166.688\n", 3782 | "2017-11-04 2017-11-04 1284.77485 309.381 57.358 366.739\n", 3783 | "2017-11-05 2017-11-05 1193.85495 259.371 25.261 284.632\n", 3784 | "2017-11-06 2017-11-06 1505.67173 98.420 40.298 138.718\n", 3785 | "2017-11-07 2017-11-07 1545.63945 148.785 27.302 176.087\n", 3786 | "2017-11-08 2017-11-08 1547.48171 62.667 18.332 80.999\n", 3787 | "2017-11-09 2017-11-09 1557.63091 210.488 15.289 225.777\n", 3788 | "2017-11-10 2017-11-10 1560.41732 627.814 12.786 640.600\n", 3789 | "2017-11-11 2017-11-11 1346.43916 530.919 18.164 549.083\n", 3790 | "2017-11-12 2017-11-12 1241.51495 235.422 15.732 251.154\n", 3791 | "2017-11-13 2017-11-13 1538.83561 205.072 36.997 242.069\n", 3792 | "2017-11-14 2017-11-14 1577.45348 294.483 38.772 333.255\n", 3793 | "2017-11-15 2017-11-15 1562.03367 98.029 25.802 123.831\n", 3794 | "2017-11-16 2017-11-16 1574.73479 179.491 18.145 197.636\n", 3795 | "2017-11-17 2017-11-17 1552.21780 204.464 32.919 237.383\n", 3796 | "2017-11-18 2017-11-18 1369.91888 606.663 18.880 625.543\n", 3797 | "2017-11-19 2017-11-19 1268.00462 721.238 28.651 749.889\n", 3798 | "2017-11-20 2017-11-20 1565.56805 380.284 14.922 395.206\n", 3799 | "2017-11-21 2017-11-21 1576.49592 318.591 11.240 329.831\n", 3800 | "2017-11-22 2017-11-22 1558.72998 588.234 46.147 634.381\n", 3801 | "2017-11-23 2017-11-23 1581.78770 730.728 42.047 772.775\n", 3802 | "2017-11-24 2017-11-24 1528.12694 313.086 30.441 343.527\n", 3803 | "2017-11-25 2017-11-25 1333.15585 238.670 13.552 252.222\n", 3804 | "2017-11-26 2017-11-26 1276.09818 599.330 22.586 621.916\n", 3805 | "2017-11-27 2017-11-27 1602.17797 743.280 21.983 765.263\n", 3806 | "2017-11-28 2017-11-28 1615.38061 514.931 19.658 534.589\n", 3807 | "2017-11-29 2017-11-29 1603.98435 99.653 24.155 123.808\n", 3808 | "2017-11-30 2017-11-30 1617.03309 66.389 14.314 80.703\n" 3809 | ] 3810 | } 3811 | ], 3812 | "source": [ 3813 | "# Indexing and slicing in historical indexes\n", 3814 | "\n", 3815 | "result = pd.read_csv(\"Data_v2.csv\")\n", 3816 | "\n", 3817 | "final = result.set_index(pd.DatetimeIndex(result[\"Date\"].values))\n", 3818 | "\n", 3819 | "# Indexing\n", 3820 | "print(final.loc[\"2012-2-2\"], \"\\n\")\n", 3821 | "\n", 3822 | "# Slicing\n", 3823 | "print(final.loc[\"2017-10-31\":\"2017-11\"])" 3824 | ] 3825 | }, 3826 | { 3827 | "cell_type": "code", 3828 | "execution_count": 60, 3829 | "metadata": {}, 3830 | "outputs": [ 3831 | { 3832 | "data": { 3833 | "text/plain": [ 3834 | "" 3848 | ] 3849 | }, 3850 | "execution_count": 60, 3851 | "metadata": {}, 3852 | "output_type": "execute_result" 3853 | } 3854 | ], 3855 | "source": [ 3856 | "# Information about Date.\n", 3857 | "result.info" 3858 | ] 3859 | }, 3860 | { 3861 | "cell_type": "code", 3862 | "execution_count": 61, 3863 | "metadata": {}, 3864 | "outputs": [ 3865 | { 3866 | "data": { 3867 | "text/plain": [ 3868 | "1338.6758355897814" 3869 | ] 3870 | }, 3871 | "execution_count": 61, 3872 | "metadata": {}, 3873 | "output_type": "execute_result" 3874 | } 3875 | ], 3876 | "source": [ 3877 | "# Calculate the average of a column.\n", 3878 | "result[\"Consumption\"].mean()" 3879 | ] 3880 | }, 3881 | { 3882 | "cell_type": "code", 3883 | "execution_count": 62, 3884 | "metadata": {}, 3885 | "outputs": [ 3886 | { 3887 | "data": { 3888 | "text/plain": [ 3889 | "165.7757102347912" 3890 | ] 3891 | }, 3892 | "execution_count": 62, 3893 | "metadata": {}, 3894 | "output_type": "execute_result" 3895 | } 3896 | ], 3897 | "source": [ 3898 | "# standard deviation.\n", 3899 | "result[\"Consumption\"].std()" 3900 | ] 3901 | }, 3902 | { 3903 | "cell_type": "code", 3904 | "execution_count": 63, 3905 | "metadata": {}, 3906 | "outputs": [ 3907 | { 3908 | "data": { 3909 | "text/plain": [ 3910 | "-0.30883182959773414" 3911 | ] 3912 | }, 3913 | "execution_count": 63, 3914 | "metadata": {}, 3915 | "output_type": "execute_result" 3916 | } 3917 | ], 3918 | "source": [ 3919 | "# Coordination between changes.\n", 3920 | "result[\"Wind\"].corr(result[\"Solar\"])" 3921 | ] 3922 | }, 3923 | { 3924 | "cell_type": "code", 3925 | "execution_count": 64, 3926 | "metadata": {}, 3927 | "outputs": [ 3928 | { 3929 | "name": "stdout", 3930 | "output_type": "stream", 3931 | "text": [ 3932 | "max: 851.556 \n", 3933 | "min: 21.478\n" 3934 | ] 3935 | } 3936 | ], 3937 | "source": [ 3938 | "# The highest and lowest value of a column.\n", 3939 | "max_ = result[\"Wind+Solar\"].max()\n", 3940 | "min_ = result[\"Wind+Solar\"].min()\n", 3941 | "print(f\"max: {max_} \\nmin: {min_}\")" 3942 | ] 3943 | }, 3944 | { 3945 | "cell_type": "code", 3946 | "execution_count": null, 3947 | "metadata": {}, 3948 | "outputs": [], 3949 | "source": [] 3950 | } 3951 | ], 3952 | "metadata": { 3953 | "kernelspec": { 3954 | "display_name": "Python 3", 3955 | "language": "python", 3956 | "name": "python3" 3957 | }, 3958 | "language_info": { 3959 | "codemirror_mode": { 3960 | "name": "ipython", 3961 | "version": 3 3962 | }, 3963 | "file_extension": ".py", 3964 | "mimetype": "text/x-python", 3965 | "name": "python", 3966 | "nbconvert_exporter": "python", 3967 | "pygments_lexer": "ipython3", 3968 | "version": "3.8.5" 3969 | } 3970 | }, 3971 | "nbformat": 4, 3972 | "nbformat_minor": 4 3973 | } 3974 | --------------------------------------------------------------------------------