{
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330877</td>\n",
" <td>8.4583</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>male</td>\n",
" <td>54.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17463</td>\n",
" <td>51.8625</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Master. Gosta Leonard</td>\n",
" <td>male</td>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>349909</td>\n",
" <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
" <td>female</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>347742</td>\n",
" <td>11.1333</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
" <td>female</td>\n",
" <td>14.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>237736</td>\n",
" <td>30.0708</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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."
]
}
],
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"datacleaner": {
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