{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "![](images/EscUpmPolit_p.gif \"UPM\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "# Course Notes for Learning Intelligent Systems" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## [Introduction to Preprocessing](00_Intro_Preprocessing.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Initial Check with Pandas\n", "\n", "We can start with a quick quality check." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Load and check data\n", "Check which data you are loading." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
\n", "
" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "5 6 0 3 \n", "6 7 0 1 \n", "7 8 0 3 \n", "8 9 1 3 \n", "9 10 1 2 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "5 Moran, Mr. James male NaN 0 \n", "6 McCarthy, Mr. Timothy J male 54.0 0 \n", "7 Palsson, Master. Gosta Leonard male 2.0 3 \n", "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n", "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S \n", "5 0 330877 8.4583 NaN Q \n", "6 0 17463 51.8625 E46 S \n", "7 1 349909 21.0750 NaN S \n", "8 2 347742 11.1333 NaN S \n", "9 0 237736 30.0708 NaN C " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.read_csv('https://raw.githubusercontent.com/gsi-upm/sitc/master/ml2/data-titanic/train.csv')\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Check number of columns and rows" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "(891, 12)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Check names and types of columns\n", "Check the data and type, for example if dates are of strings or what." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n", " 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n", " dtype='object')\n" ] }, { "data": { "text/plain": [ "PassengerId int64\n", "Survived int64\n", "Pclass int64\n", "Name object\n", "Sex object\n", "Age float64\n", "SibSp int64\n", "Parch int64\n", "Ticket object\n", "Fare float64\n", "Cabin object\n", "Embarked object\n", "dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get column names\n", "print(df.columns)\n", "# Get column data types\n", "df.dtypes" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Check if the column is unique" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PassengerId is unique: True\n", "Survived is unique: False\n", "Pclass is unique: False\n", "Name is unique: True\n", "Sex is unique: False\n", "Age is unique: False\n", "SibSp is unique: False\n", "Parch is unique: False\n", "Ticket is unique: False\n", "Fare is unique: False\n", "Cabin is unique: False\n", "Embarked is unique: False\n" ] } ], "source": [ "for i in column_names:\n", " print('{} is unique: {}'.format(i, df[i].is_unique))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Check if the dataframe has an index\n", "We will need it to do joins or merges." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=891, step=1)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check if there is an index. If not, you will get 'AtributeError: function object has no atribute index'\n", "df.index" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n", " 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n", " 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n", " 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n", " 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n", " 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n", " 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n", " 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n", " 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n", " 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n", " 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n", " 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n", " 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n", " 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n", " 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n", " 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n", " 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,\n", " 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,\n", " 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,\n", " 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n", " 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,\n", " 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,\n", " 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,\n", " 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,\n", " 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,\n", " 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,\n", " 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,\n", " 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,\n", " 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,\n", " 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,\n", " 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,\n", " 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,\n", " 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,\n", " 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,\n", " 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,\n", " 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,\n", " 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,\n", " 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493,\n", " 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506,\n", " 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519,\n", " 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532,\n", " 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545,\n", " 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558,\n", " 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571,\n", " 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584,\n", " 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597,\n", " 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610,\n", " 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623,\n", " 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636,\n", " 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649,\n", " 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662,\n", " 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675,\n", " 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688,\n", " 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701,\n", " 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714,\n", " 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727,\n", " 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740,\n", " 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753,\n", " 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766,\n", " 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779,\n", " 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792,\n", " 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805,\n", " 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818,\n", " 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831,\n", " 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844,\n", " 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857,\n", " 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870,\n", " 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883,\n", " 884, 885, 886, 887, 888, 889, 890])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# # Check the index values\n", "df.index.values" ] }, { "cell_type": "raw", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# If index does not exist\n", "df.set_index('column_name_to_use', inplace=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "PassengerId 0\n", "Survived 0\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 177\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 0\n", "Cabin 687\n", "Embarked 2\n", "dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Count missing vales per column\n", "df.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "# References\n", "* [Cleaning and Prepping Data with Python for Data Science — Best Practices and Helpful Packages](https://medium.com/@rrfd/cleaning-and-prepping-data-with-python-for-data-science-best-practices-and-helpful-packages-af1edfbe2a3), DeFilippi, 2019, \n", "* [Data Preprocessing for Machine learning in Python, GeeksForGeeks](https://www.geeksforgeeks.org/data-preprocessing-machine-learning-python/)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Licence\n", "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", "\n", "© Carlos A. Iglesias, Universidad Politécnica de Madrid." ] } ], "metadata": { "celltoolbar": "Slideshow", "datacleaner": { "position": { "top": "50px" }, "python": { "varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])" }, "window_display": false }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 4 }