Spaces:
Sleeping
Sleeping
File size: 48,903 Bytes
bf6ba00 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "4ba6aba8"
},
"source": [
"# 🤖 **Data Collection, Creation, Storage, and Processing**\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jpASMyIQMaAq"
},
"source": [
"## **1.** 📦 Install required packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "f48c8f8c",
"outputId": "4f196026-072b-44eb-a94e-cc21132bfa7e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.12/dist-packages (4.13.5)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
"Requirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n",
"Requirement already satisfied: textblob in /usr/local/lib/python3.12/dist-packages (0.19.0)\n",
"Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4) (2.8.3)\n",
"Requirement already satisfied: typing-extensions>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4) (4.15.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.3)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.62.1)\n",
"Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.5.0)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.0)\n",
"Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)\n",
"Requirement already satisfied: nltk>=3.9 in /usr/local/lib/python3.12/dist-packages (from textblob) (3.9.1)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (8.3.1)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (1.5.3)\n",
"Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (2025.11.3)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (4.67.3)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n"
]
}
],
"source": [
"!pip install beautifulsoup4 pandas matplotlib seaborn numpy textblob"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lquNYCbfL9IM"
},
"source": [
"## **2.** ⛏ Web-scrape all book titles, prices, and ratings from books.toscrape.com"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0IWuNpxxYDJF"
},
"source": [
"### *a. Initial setup*\n",
"Define the base url of the website you will scrape as well as how and what you will scrape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "91d52125"
},
"outputs": [],
"source": [
"import requests\n",
"from bs4 import BeautifulSoup\n",
"import pandas as pd\n",
"import time\n",
"\n",
"base_url = \"https://books.toscrape.com/catalogue/page-{}.html\"\n",
"headers = {\"User-Agent\": \"Mozilla/5.0\"}\n",
"\n",
"titles, prices, ratings = [], [], []"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oCdTsin2Yfp3"
},
"source": [
"### *b. Fill titles, prices, and ratings from the web pages*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xqO5Y3dnYhxt"
},
"outputs": [],
"source": [
"# Loop through all 50 pages\n",
"for page in range(1, 51):\n",
" url = base_url.format(page)\n",
" response = requests.get(url, headers=headers)\n",
" soup = BeautifulSoup(response.content, \"html.parser\")\n",
" books = soup.find_all(\"article\", class_=\"product_pod\")\n",
"\n",
" for book in books:\n",
" titles.append(book.h3.a[\"title\"])\n",
" prices.append(float(book.find(\"p\", class_=\"price_color\").text[1:]))\n",
" ratings.append(book.p.get(\"class\")[1])\n",
"\n",
" time.sleep(0.5) # polite scraping delay"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T0TOeRC4Yrnn"
},
"source": [
"### *c. ✋🏻🛑⛔️ Create a dataframe df_books that contains the now complete \"title\", \"price\", and \"rating\" objects*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "l5FkkNhUYTHh"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"df_books = pd.DataFrame({\n",
" \"title\": titles,\n",
" \"price\": prices,\n",
" \"rating\": ratings\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "duI5dv3CZYvF"
},
"source": [
"### *d. Save web-scraped dataframe either as a CSV or Excel file*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lC1U_YHtZifh"
},
"outputs": [],
"source": [
"# 💾 Save to CSV\n",
"df_books.to_csv(\"books_data.csv\", index=False)\n",
"\n",
"# 💾 Or save to Excel\n",
"# df_books.to_excel(\"books_data.xlsx\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qMjRKMBQZlJi"
},
"source": [
"### *e. ✋🏻🛑⛔️ View first fiew lines*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "O_wIvTxYZqCK",
"outputId": "a0a39578-2938-4332-cc23-c868d845b4e3"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" title price rating\n",
"0 A Light in the Attic 51.77 Three\n",
"1 Tipping the Velvet 53.74 One\n",
"2 Soumission 50.10 One\n",
"3 Sharp Objects 47.82 Four\n",
"4 Sapiens: A Brief History of Humankind 54.23 Five"
],
"text/html": [
"\n",
" <div id=\"df-a8a3ffbb-0352-416d-9aad-910fe4bdc396\" class=\"colab-df-container\">\n",
" <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>title</th>\n",
" <th>price</th>\n",
" <th>rating</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A Light in the Attic</td>\n",
" <td>51.77</td>\n",
" <td>Three</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Tipping the Velvet</td>\n",
" <td>53.74</td>\n",
" <td>One</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Soumission</td>\n",
" <td>50.10</td>\n",
" <td>One</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Sharp Objects</td>\n",
" <td>47.82</td>\n",
" <td>Four</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Sapiens: A Brief History of Humankind</td>\n",
" <td>54.23</td>\n",
" <td>Five</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8a3ffbb-0352-416d-9aad-910fe4bdc396')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-a8a3ffbb-0352-416d-9aad-910fe4bdc396 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-a8a3ffbb-0352-416d-9aad-910fe4bdc396');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df_books",
"summary": "{\n \"name\": \"df_books\",\n \"rows\": 1000,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 999,\n \"samples\": [\n \"The Grownup\",\n \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n \"Ayumi's Violin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.446689669952772,\n \"min\": 10.0,\n \"max\": 59.99,\n \"num_unique_values\": 903,\n \"samples\": [\n 19.73,\n 55.65,\n 46.31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"One\",\n \"Two\",\n \"Four\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 40
}
],
"source": [
"df_books.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p-1Pr2szaqLk"
},
"source": [
"## **3.** 🧩 Create a meaningful connection between real & synthetic datasets"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SIaJUGIpaH4V"
},
"source": [
"### *a. Initial setup*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-gPXGcRPuV_9"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import random\n",
"from datetime import datetime\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"random.seed(2025)\n",
"np.random.seed(2025)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pY4yCoIuaQqp"
},
"source": [
"### *b. Generate popularity scores based on rating (with some randomness) with a generate_popularity_score function*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mnd5hdAbaNjz"
},
"outputs": [],
"source": [
"def generate_popularity_score(rating):\n",
" base = {\"One\": 2, \"Two\": 3, \"Three\": 3, \"Four\": 4, \"Five\": 4}.get(rating, 3)\n",
" trend_factor = random.choices([-1, 0, 1], weights=[1, 3, 2])[0]\n",
" return int(np.clip(base + trend_factor, 1, 5))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n4-TaNTFgPak"
},
"source": [
"### *c. ✋🏻🛑⛔️ Run the function to create a \"popularity_score\" column from \"rating\"*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "V-G3OCUCgR07"
},
"outputs": [],
"source": [
"df_books[\"popularity_score\"] = df_books[\"rating\"].apply(generate_popularity_score)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HnngRNTgacYt"
},
"source": [
"### *d. Decide on the sentiment_label based on the popularity score with a get_sentiment function*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kUtWmr8maZLZ"
},
"outputs": [],
"source": [
"def get_sentiment(popularity_score):\n",
" if popularity_score <= 2:\n",
" return \"negative\"\n",
" elif popularity_score == 3:\n",
" return \"neutral\"\n",
" else:\n",
" return \"positive\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HF9F9HIzgT7Z"
},
"source": [
"### *e. ✋🏻🛑⛔️ Run the function to create a \"sentiment_label\" column from \"popularity_score\"*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tafQj8_7gYCG"
},
"outputs": [],
"source": [
"df_books[\"sentiment_label\"] = df_books[\"popularity_score\"].apply(get_sentiment)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T8AdKkmASq9a"
},
"source": [
"## **4.** 📈 Generate synthetic book sales data of 18 months"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OhXbdGD5fH0c"
},
"source": [
"### *a. Create a generate_sales_profit function that would generate sales patterns based on sentiment_label (with some randomness)*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qkVhYPXGbgEn"
},
"outputs": [],
"source": [
"def generate_sales_profile(sentiment):\n",
" months = pd.date_range(end=datetime.today(), periods=18, freq=\"M\")\n",
"\n",
" if sentiment == \"positive\":\n",
" base = random.randint(200, 300)\n",
" trend = np.linspace(base, base + random.randint(20, 60), len(months))\n",
" elif sentiment == \"negative\":\n",
" base = random.randint(20, 80)\n",
" trend = np.linspace(base, base - random.randint(10, 30), len(months))\n",
" else: # neutral\n",
" base = random.randint(80, 160)\n",
" trend = np.full(len(months), base + random.randint(-10, 10))\n",
"\n",
" seasonality = 10 * np.sin(np.linspace(0, 3 * np.pi, len(months)))\n",
" noise = np.random.normal(0, 5, len(months))\n",
" monthly_sales = np.clip(trend + seasonality + noise, a_min=0, a_max=None).astype(int)\n",
"\n",
" return list(zip(months.strftime(\"%Y-%m\"), monthly_sales))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L2ak1HlcgoTe"
},
"source": [
"### *b. Run the function as part of building sales_data*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SlJ24AUafoDB"
},
"outputs": [],
"source": [
"sales_data = []\n",
"for _, row in df_books.iterrows():\n",
" records = generate_sales_profile(row[\"sentiment_label\"])\n",
" for month, units in records:\n",
" sales_data.append({\n",
" \"title\": row[\"title\"],\n",
" \"month\": month,\n",
" \"units_sold\": units,\n",
" \"sentiment_label\": row[\"sentiment_label\"]\n",
" })"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4IXZKcCSgxnq"
},
"source": [
"### *c. ✋🏻🛑⛔️ Create a df_sales DataFrame from sales_data*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wcN6gtiZg-ws"
},
"outputs": [],
"source": [
"df_sales = pd.DataFrame(sales_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EhIjz9WohAmZ"
},
"source": [
"### *d. Save df_sales as synthetic_sales_data.csv & view first few lines*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MzbZvLcAhGaH",
"outputId": "cbd2b207-876f-42ce-8818-1ee4457efa97"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" title month units_sold sentiment_label\n",
"0 A Light in the Attic 2024-09 100 neutral\n",
"1 A Light in the Attic 2024-10 109 neutral\n",
"2 A Light in the Attic 2024-11 102 neutral\n",
"3 A Light in the Attic 2024-12 107 neutral\n",
"4 A Light in the Attic 2025-01 108 neutral\n"
]
}
],
"source": [
"df_sales.to_csv(\"synthetic_sales_data.csv\", index=False)\n",
"\n",
"print(df_sales.head())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7g9gqBgQMtJn"
},
"source": [
"## **5.** 🎯 Generate synthetic customer reviews"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Gi4y9M9KuDWx"
},
"source": [
"### *a. ✋🏻🛑⛔️ Ask ChatGPT to create a list of 50 distinct generic book review texts for the sentiment labels \"positive\", \"neutral\", and \"negative\" called synthetic_reviews_by_sentiment*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "b3cd2a50"
},
"outputs": [],
"source": [
"synthetic_reviews_by_sentiment = {\n",
" \"positive\": [\n",
" \"I absolutely loved this book, it was amazing.\",\n",
" \"A fantastic read with great characters.\",\n",
" \"Really enjoyable and well written.\",\n",
" \"An excellent story from start to finish.\",\n",
" \"I couldn't put it down, highly recommend.\",\n",
" \"Brilliant and engaging narrative.\",\n",
" \"A masterpiece, truly inspiring.\",\n",
" \"Very entertaining and satisfying.\",\n",
" \"Loved every chapter of this book.\",\n",
" \"A great experience overall.\",\n",
" \"Superb writing and compelling plot.\",\n",
" \"One of the best books I've read.\",\n",
" \"Amazing storytelling and depth.\",\n",
" \"Highly enjoyable and emotional.\",\n",
" \"A very positive reading experience.\",\n",
" \"Incredible and memorable story.\",\n",
" \"Absolutely worth reading.\"\n",
" ],\n",
" \"neutral\": [\n",
" \"The book was okay, nothing special.\",\n",
" \"An average read with some good moments.\",\n",
" \"It was fine, but not very memorable.\",\n",
" \"Decent but not outstanding.\",\n",
" \"A fairly typical story.\",\n",
" \"Some parts were interesting, others not.\",\n",
" \"It was neither good nor bad.\",\n",
" \"A standard book with no surprises.\",\n",
" \"Moderately engaging but slow at times.\",\n",
" \"An acceptable read overall.\",\n",
" \"Nothing particularly stood out.\",\n",
" \"It had its moments but was inconsistent.\",\n",
" \"Just an average experience.\",\n",
" \"Okay for passing time.\",\n",
" \"Not bad, not great either.\",\n",
" \"Fairly predictable storyline.\"\n",
" ],\n",
" \"negative\": [\n",
" \"I didn't enjoy this book at all.\",\n",
" \"The story was boring and slow.\",\n",
" \"Very disappointing read.\",\n",
" \"Poorly written and hard to follow.\",\n",
" \"I struggled to finish it.\",\n",
" \"Not engaging and quite dull.\",\n",
" \"The plot made little sense.\",\n",
" \"A waste of time in my opinion.\",\n",
" \"Characters were flat and uninteresting.\",\n",
" \"I would not recommend this book.\",\n",
" \"Very underwhelming and forgettable.\",\n",
" \"Bad pacing and weak story.\",\n",
" \"It failed to keep my interest.\",\n",
" \"Quite frustrating to read.\",\n",
" \"Not worth reading.\",\n",
" \"I expected much better.\"\n",
" ]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fQhfVaDmuULT"
},
"source": [
"### *b. Generate 10 reviews per book using random sampling from the corresponding 50*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "l2SRc3PjuTGM"
},
"outputs": [],
"source": [
"review_rows = []\n",
"for _, row in df_books.iterrows():\n",
" title = row['title']\n",
" sentiment_label = row['sentiment_label']\n",
" review_pool = synthetic_reviews_by_sentiment[sentiment_label]\n",
" sampled_reviews = random.sample(review_pool, 10)\n",
" for review_text in sampled_reviews:\n",
" review_rows.append({\n",
" \"title\": title,\n",
" \"sentiment_label\": sentiment_label,\n",
" \"review_text\": review_text,\n",
" \"rating\": row['rating'],\n",
" \"popularity_score\": row['popularity_score']\n",
" })"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bmJMXF-Bukdm"
},
"source": [
"### *c. Create the final dataframe df_reviews & save it as synthetic_book_reviews.csv*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZUKUqZsuumsp"
},
"outputs": [],
"source": [
"df_reviews = pd.DataFrame(review_rows)\n",
"df_reviews.to_csv(\"synthetic_book_reviews.csv\", index=False)"
]
},
{
"cell_type": "markdown",
"source": [
"### *c. inputs for R*"
],
"metadata": {
"id": "_602pYUS3gY5"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3946e521",
"outputId": "6a29f9d9-0dfc-4db1-896a-17e29119424a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"✅ Wrote synthetic_title_level_features.csv\n",
"✅ Wrote synthetic_monthly_revenue_series.csv\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd # Ensure pandas is imported locally\n",
"\n",
"def _safe_num(s):\n",
" # Ensure s is a Series before applying string operations\n",
" if not isinstance(s, pd.Series):\n",
" s = pd.Series(s)\n",
" return pd.to_numeric(\n",
" s.astype(str).str.replace(r\"[^0-9.]\", \"\", regex=True),\n",
" errors=\"coerce\"\n",
" )\n",
"\n",
"# --- Clean book metadata (price/rating) ---\n",
"df_books_r = df_books.copy()\n",
"if not df_books_r.empty:\n",
" if \"price\" in df_books_r.columns:\n",
" df_books_r[\"price\"] = _safe_num(df_books_r[\"price\"])\n",
" if \"rating\" in df_books_r.columns:\n",
" df_books_r[\"rating\"] = _safe_num(df_books_r[\"rating\"])\n",
" if \"title\" in df_books_r.columns:\n",
" df_books_r[\"title\"] = df_books_r[\"title\"].astype(str).str.strip()\n",
"\n",
"\n",
"# --- Clean sales ---\n",
"df_sales_r = df_sales.copy()\n",
"if not df_sales_r.empty:\n",
" if \"title\" in df_sales_r.columns:\n",
" df_sales_r[\"title\"] = df_sales_r[\"title\"].astype(str).str.strip()\n",
" if \"month\" in df_sales_r.columns:\n",
" df_sales_r[\"month\"] = pd.to_datetime(df_sales_r[\"month\"], errors=\"coerce\")\n",
" if \"units_sold\" in df_sales_r.columns:\n",
" df_sales_r[\"units_sold\"] = _safe_num(df_sales_r[\"units_sold\"])\n",
"else:\n",
" # If df_sales_r is empty (no columns), create an empty one with expected columns\n",
" df_sales_r = pd.DataFrame(columns=['title', 'month', 'units_sold', 'sentiment_label'])\n",
"\n",
"\n",
"# --- Clean reviews ---\n",
"df_reviews_r = df_reviews.copy()\n",
"if not df_reviews_r.empty:\n",
" if \"title\" in df_reviews_r.columns:\n",
" df_reviews_r[\"title\"] = df_reviews_r[\"title\"].astype(str).str.strip()\n",
" if \"sentiment_label\" in df_reviews_r.columns:\n",
" df_reviews_r[\"sentiment_label\"] = df_reviews_r[\"sentiment_label\"].astype(str).str.lower().str.strip()\n",
" if \"rating\" in df_reviews_r.columns:\n",
" df_reviews_r[\"rating\"] = _safe_num(df_reviews_r[\"rating\"])\n",
" if \"popularity_score\" in df_reviews_r.columns:\n",
" df_reviews_r[\"popularity_score\"] = _safe_num(df_reviews_r[\"popularity_score\"])\n",
"else:\n",
" # If df_reviews_r is empty (no columns), create an empty one with expected columns\n",
" df_reviews_r = pd.DataFrame(columns=['title', 'sentiment_label', 'review_text', 'rating', 'popularity_score'])\n",
"\n",
"\n",
"# --- Sentiment shares per title (from reviews) ---\n",
"if not df_reviews_r.empty and 'title' in df_reviews_r.columns and 'sentiment_label' in df_reviews_r.columns:\n",
" sent_counts = (\n",
" df_reviews_r.groupby([\"title\", \"sentiment_label\"])\n",
" .size()\n",
" .unstack(fill_value=0)\n",
" )\n",
"else:\n",
" sent_counts = pd.DataFrame(columns=['title', 'positive', 'neutral', 'negative'])\n",
"\n",
"for lab in [\"positive\", \"neutral\", \"negative\"]:\n",
" if lab not in sent_counts.columns:\n",
" sent_counts[lab] = 0\n",
"\n",
"if not sent_counts.empty and 'total_reviews' not in sent_counts.columns:\n",
" sent_counts[\"total_reviews\"] = sent_counts[[\"positive\", \"neutral\", \"negative\"]].sum(axis=1)\n",
" den = sent_counts[\"total_reviews\"].replace(0, np.nan)\n",
" sent_counts[\"share_positive\"] = sent_counts[\"positive\"] / den\n",
" sent_counts[\"share_neutral\"] = sent_counts[\"neutral\"] / den\n",
" sent_counts[\"share_negative\"] = sent_counts[\"negative\"] / den\n",
" sent_counts = sent_counts.reset_index()\n",
"elif sent_counts.empty:\n",
" sent_counts = pd.DataFrame(columns=['title', 'share_positive', 'share_neutral', 'share_negative', 'total_reviews'])\n",
"\n",
"\n",
"# --- Sales aggregation per title ---\n",
"if not df_sales_r.empty and 'title' in df_sales_r.columns:\n",
" sales_by_title = (\n",
" df_sales_r.dropna(subset=[\"title\"])\n",
" .groupby(\"title\", as_index=False)\n",
" .agg(\n",
" months_observed=(\"month\", \"nunique\"),\n",
" avg_units_sold=(\"units_sold\", \"mean\"),\n",
" total_units_sold=(\"units_sold\", \"sum\"),\n",
" )\n",
" )\n",
"else:\n",
" sales_by_title = pd.DataFrame(columns=['title', 'months_observed', 'avg_units_sold', 'total_units_sold'])\n",
"\n",
"\n",
"# --- Title-level features (join sales + books + sentiment) ---\n",
"# Initialize df_title as an empty DataFrame with all expected columns\n",
"df_title = pd.DataFrame(columns=[\n",
" 'title', 'months_observed', 'avg_units_sold', 'total_units_sold',\n",
" 'price', 'rating', 'share_positive', 'share_neutral', 'share_negative',\n",
" 'total_reviews', 'avg_revenue', 'total_revenue'\n",
"])\n",
"\n",
"if not sales_by_title.empty and 'title' in sales_by_title.columns and \\\n",
" not df_books_r.empty and 'title' in df_books_r.columns and \\\n",
" not sent_counts.empty and 'title' in sent_counts.columns:\n",
"\n",
" df_title = (\n",
" sales_by_title\n",
" .merge(df_books_r[[\"title\", \"price\", \"rating\"]], on=\"title\", how=\"left\")\n",
" .merge(sent_counts[[\"title\", \"share_positive\", \"share_neutral\", \"share_negative\", \"total_reviews\"]],\n",
" on=\"title\", how=\"left\")\n",
" )\n",
"\n",
" if not df_title.empty:\n",
" if 'avg_units_sold' in df_title.columns and 'price' in df_title.columns:\n",
" df_title[\"avg_revenue\"] = df_title[\"avg_units_sold\"] * df_title[\"price\"]\n",
" if 'total_units_sold' in df_title.columns and 'price' in df_title.columns:\n",
" df_title[\"total_revenue\"] = df_title[\"total_units_sold\"] * df_title[\"price\"]\n",
"\n",
"df_title.to_csv(\"synthetic_title_level_features.csv\", index=False)\n",
"print(\"✅ Wrote synthetic_title_level_features.csv\")\n",
"\n",
"\n",
"# --- Monthly revenue series (proxy: units_sold * price) ---\n",
"monthly_rev = pd.DataFrame() # Initialize as empty\n",
"if not df_sales_r.empty and 'title' in df_sales_r.columns and \\\n",
" not df_books_r.empty and 'title' in df_books_r.columns and 'price' in df_books_r.columns:\n",
"\n",
" monthly_rev = (\n",
" df_sales_r.merge(df_books_r[[\"title\", \"price\"]], on=\"title\", how=\"left\")\n",
" )\n",
" if not monthly_rev.empty and 'units_sold' in monthly_rev.columns and 'price' in monthly_rev.columns:\n",
" monthly_rev[\"revenue\"] = monthly_rev[\"units_sold\"] * monthly_rev[\"price\"]\n",
"\n",
"df_monthly = pd.DataFrame(columns=['month', 'total_revenue']) # Initialize df_monthly\n",
"\n",
"if not monthly_rev.empty and 'month' in monthly_rev.columns and 'revenue' in monthly_rev.columns:\n",
" df_monthly = (\n",
" monthly_rev.dropna(subset=[\"month\"])\n",
" .groupby(\"month\", as_index=False)[\"revenue\"]\n",
" .sum()\n",
" .rename(columns={\"revenue\": \"total_revenue\"})\n",
" .sort_values(\"month\")\n",
" )\n",
" # if revenue is all NA (e.g., missing price), fallback to units_sold as a teaching proxy\n",
" if 'total_revenue' in df_monthly.columns and df_monthly[\"total_revenue\"].notna().sum() == 0 and \\\n",
" not df_sales_r.empty and 'month' in df_sales_r.columns and 'units_sold' in df_sales_r.columns:\n",
" df_monthly = (\n",
" df_sales_r.dropna(subset=[\"month\"])\n",
" .groupby(\"month\", as_index=False)[\"units_sold\"]\n",
" .sum()\n",
" .rename(columns={\"units_sold\": \"total_revenue\"})\n",
" .sort_values(\"month\")\n",
" )\n",
"\n",
"if not df_monthly.empty and 'month' in df_monthly.columns:\n",
" df_monthly[\"month\"] = pd.to_datetime(df_monthly[\"month\"], errors=\"coerce\").dt.strftime(\"%Y-%m-%d\")\n",
"\n",
"df_monthly.to_csv(\"synthetic_monthly_revenue_series.csv\", index=False)\n",
"print(\"✅ Wrote synthetic_monthly_revenue_series.csv\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RYvGyVfXuo54"
},
"source": [
"### *d. ✋🏻🛑⛔️ View the first few lines*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
},
"id": "xfE8NMqOurKo",
"outputId": "c21a60f1-b8f4-4586-fabc-19c1f81a14d4"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" title month units_sold sentiment_label\n",
"0 A Light in the Attic 2024-09 100 neutral\n",
"1 A Light in the Attic 2024-10 109 neutral\n",
"2 A Light in the Attic 2024-11 102 neutral"
],
"text/html": [
"\n",
" <div id=\"df-6cca1a9e-0f2d-41b7-a73f-4ee410a40824\" class=\"colab-df-container\">\n",
" <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>title</th>\n",
" <th>month</th>\n",
" <th>units_sold</th>\n",
" <th>sentiment_label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A Light in the Attic</td>\n",
" <td>2024-09</td>\n",
" <td>100</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A Light in the Attic</td>\n",
" <td>2024-10</td>\n",
" <td>109</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A Light in the Attic</td>\n",
" <td>2024-11</td>\n",
" <td>102</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6cca1a9e-0f2d-41b7-a73f-4ee410a40824')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-6cca1a9e-0f2d-41b7-a73f-4ee410a40824 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-6cca1a9e-0f2d-41b7-a73f-4ee410a40824');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df_sales",
"summary": "{\n \"name\": \"df_sales\",\n \"rows\": 18000,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 999,\n \"samples\": [\n \"The Grownup\",\n \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n \"Ayumi's Violin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"month\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 18,\n \"samples\": [\n \"2024-09\",\n \"2024-10\",\n \"2025-05\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"units_sold\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 98,\n \"min\": 0,\n \"max\": 362,\n \"num_unique_values\": 354,\n \"samples\": [\n 214,\n 289,\n 205\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment_label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"negative\",\n \"positive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 59
}
],
"source": [
"df_sales.head(3)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"jpASMyIQMaAq",
"lquNYCbfL9IM",
"0IWuNpxxYDJF",
"oCdTsin2Yfp3",
"T0TOeRC4Yrnn",
"duI5dv3CZYvF",
"qMjRKMBQZlJi",
"p-1Pr2szaqLk",
"SIaJUGIpaH4V",
"pY4yCoIuaQqp",
"n4-TaNTFgPak",
"HnngRNTgacYt",
"HF9F9HIzgT7Z",
"T8AdKkmASq9a",
"OhXbdGD5fH0c",
"L2ak1HlcgoTe",
"4IXZKcCSgxnq",
"EhIjz9WohAmZ",
"Gi4y9M9KuDWx",
"fQhfVaDmuULT",
"bmJMXF-Bukdm",
"RYvGyVfXuo54"
],
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |