Spaces:
Sleeping
Sleeping
File size: 56,751 Bytes
567bb77 |
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 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"TU258 - Lab 2-2 - Data Exploration\n",
"\n",
"This lab gives an example of using Pandas dataframes for analysing data\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#import pandas\n",
"import pandas as pd\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"#reading a CSV File into a Panda\n",
"videoReview = pd.read_csv(r\"C:\\\\Users\\\\Rafael\\\\Documents\\\\DataScience\\\\Data Analitics\\\\Week 3\\\\Video_Games_Sales_as_at_22_Dec_2016.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# print first 3 rows\n",
" Name Platform Year_of_Release Genre Publisher NA_Sales \\\n",
"0 Wii Sports Wii 2006.0 Sports Nintendo 41.36 \n",
"1 Super Mario Bros. NES 1985.0 Platform Nintendo 29.08 \n",
"2 Mario Kart Wii Wii 2008.0 Racing Nintendo 15.68 \n",
"\n",
" EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score Critic_Count \\\n",
"0 28.96 3.77 8.45 82.53 76.0 51.0 \n",
"1 3.58 6.81 0.77 40.24 NaN NaN \n",
"2 12.76 3.79 3.29 35.52 82.0 73.0 \n",
"\n",
" User_Score User_Count Developer Rating \n",
"0 8 322.0 Nintendo E \n",
"1 NaN NaN NaN NaN \n",
"2 8.3 709.0 Nintendo E \n"
]
}
],
"source": [
"print('# print first 3 rows')\n",
"print(videoReview[:3])\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"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>Name</th>\n",
" <th>Platform</th>\n",
" <th>Year_of_Release</th>\n",
" <th>Genre</th>\n",
" <th>Publisher</th>\n",
" <th>NA_Sales</th>\n",
" <th>EU_Sales</th>\n",
" <th>JP_Sales</th>\n",
" <th>Other_Sales</th>\n",
" <th>Global_Sales</th>\n",
" <th>Critic_Score</th>\n",
" <th>Critic_Count</th>\n",
" <th>User_Score</th>\n",
" <th>User_Count</th>\n",
" <th>Developer</th>\n",
" <th>Rating</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Wii Sports</td>\n",
" <td>Wii</td>\n",
" <td>2006.0</td>\n",
" <td>Sports</td>\n",
" <td>Nintendo</td>\n",
" <td>41.36</td>\n",
" <td>28.96</td>\n",
" <td>3.77</td>\n",
" <td>8.45</td>\n",
" <td>82.53</td>\n",
" <td>76.0</td>\n",
" <td>51.0</td>\n",
" <td>8</td>\n",
" <td>322.0</td>\n",
" <td>Nintendo</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Super Mario Bros.</td>\n",
" <td>NES</td>\n",
" <td>1985.0</td>\n",
" <td>Platform</td>\n",
" <td>Nintendo</td>\n",
" <td>29.08</td>\n",
" <td>3.58</td>\n",
" <td>6.81</td>\n",
" <td>0.77</td>\n",
" <td>40.24</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Mario Kart Wii</td>\n",
" <td>Wii</td>\n",
" <td>2008.0</td>\n",
" <td>Racing</td>\n",
" <td>Nintendo</td>\n",
" <td>15.68</td>\n",
" <td>12.76</td>\n",
" <td>3.79</td>\n",
" <td>3.29</td>\n",
" <td>35.52</td>\n",
" <td>82.0</td>\n",
" <td>73.0</td>\n",
" <td>8.3</td>\n",
" <td>709.0</td>\n",
" <td>Nintendo</td>\n",
" <td>E</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Platform Year_of_Release Genre Publisher NA_Sales \\\n",
"0 Wii Sports Wii 2006.0 Sports Nintendo 41.36 \n",
"1 Super Mario Bros. NES 1985.0 Platform Nintendo 29.08 \n",
"2 Mario Kart Wii Wii 2008.0 Racing Nintendo 15.68 \n",
"\n",
" EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score Critic_Count \\\n",
"0 28.96 3.77 8.45 82.53 76.0 51.0 \n",
"1 3.58 6.81 0.77 40.24 NaN NaN \n",
"2 12.76 3.79 3.29 35.52 82.0 73.0 \n",
"\n",
" User_Score User_Count Developer Rating \n",
"0 8 322.0 Nintendo E \n",
"1 NaN NaN NaN NaN \n",
"2 8.3 709.0 Nintendo E "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"videoReview.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"# print columns\n",
"0 Wii Sports\n",
"1 Super Mario Bros.\n",
"2 Mario Kart Wii\n",
"3 Wii Sports Resort\n",
"4 Pokemon Red/Pokemon Blue\n",
" ... \n",
"16714 Samurai Warriors: Sanada Maru\n",
"16715 LMA Manager 2007\n",
"16716 Haitaka no Psychedelica\n",
"16717 Spirits & Spells\n",
"16718 Winning Post 8 2016\n",
"Name: Name, Length: 16719, dtype: object\n"
]
}
],
"source": [
"print('----------')\n",
"print('# print columns')\n",
"print(videoReview['Name'])\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Wii Sports\n",
"1 Super Mario Bros.\n",
"2 Mario Kart Wii\n",
"3 Wii Sports Resort\n",
"4 Pokemon Red/Pokemon Blue\n",
" ... \n",
"16714 Samurai Warriors: Sanada Maru\n",
"16715 LMA Manager 2007\n",
"16716 Haitaka no Psychedelica\n",
"16717 Spirits & Spells\n",
"16718 Winning Post 8 2016\n",
"Name: Name, Length: 16719, dtype: object"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"videoReview['Name']"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"# print columns, first 5 rows\n",
"0 Wii Sports\n",
"1 Super Mario Bros.\n",
"2 Mario Kart Wii\n",
"3 Wii Sports Resort\n",
"4 Pokemon Red/Pokemon Blue\n",
"Name: Name, dtype: object\n"
]
}
],
"source": [
"print('----------')\n",
"print('# print columns, first 5 rows')\n",
"print(videoReview['Name'][:5])\n",
"\n",
"#videoReview['Name'].head(5)\n",
"#videoReview['Name'].tail(5)\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"# Platform #\n",
"Platform\n",
"PS2 2161\n",
"DS 2152\n",
"PS3 1331\n",
"Wii 1320\n",
"X360 1262\n",
"PSP 1209\n",
"PS 1197\n",
"PC 974\n",
"XB 824\n",
"GBA 822\n",
"GC 556\n",
"3DS 520\n",
"PSV 432\n",
"PS4 393\n",
"N64 319\n",
"XOne 247\n",
"SNES 239\n",
"SAT 173\n",
"WiiU 147\n",
"2600 133\n",
"NES 98\n",
"GB 98\n",
"DC 52\n",
"GEN 29\n",
"NG 12\n",
"SCD 6\n",
"WS 6\n",
"3DO 3\n",
"TG16 2\n",
"GG 1\n",
"PCFX 1\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"print('----------')\n",
"print('# Platform #')\n",
"print(videoReview['Platform'].value_counts())\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"#shape\n",
"Number of rows = 16719\n",
"Number of columns = 16\n",
"Shape = (16719, 16)\n"
]
}
],
"source": [
"print('----------')\n",
"print('#shape')\n",
"print('Number of rows = ', videoReview.shape[0])\n",
"print('Number of columns = ', videoReview.shape[1])\n",
"print('Shape = ', videoReview.shape)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"# Name #\n",
"Name\n",
"Need for Speed: Most Wanted 12\n",
"FIFA 14 9\n",
"Ratatouille 9\n",
"LEGO Marvel Super Heroes 9\n",
"Madden NFL 07 9\n",
" ..\n",
"Jewels of the Tropical Lost Island 1\n",
"Sherlock Holmes and the Mystery of Osborne House 1\n",
"The King of Fighters '95 (CD) 1\n",
"Megamind: Mega Team Unite 1\n",
"Haitaka no Psychedelica 1\n",
"Name: count, Length: 11562, dtype: int64\n"
]
}
],
"source": [
"print('----------')\n",
"print('# Name #')\n",
"print(videoReview['Name'].value_counts())\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"#head\n",
" Name Platform Year_of_Release Genre Publisher \\\n",
"0 Wii Sports Wii 2006.0 Sports Nintendo \n",
"1 Super Mario Bros. NES 1985.0 Platform Nintendo \n",
"2 Mario Kart Wii Wii 2008.0 Racing Nintendo \n",
"3 Wii Sports Resort Wii 2009.0 Sports Nintendo \n",
"4 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing Nintendo \n",
"\n",
" NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score \\\n",
"0 41.36 28.96 3.77 8.45 82.53 76.0 \n",
"1 29.08 3.58 6.81 0.77 40.24 NaN \n",
"2 15.68 12.76 3.79 3.29 35.52 82.0 \n",
"3 15.61 10.93 3.28 2.95 32.77 80.0 \n",
"4 11.27 8.89 10.22 1.00 31.37 NaN \n",
"\n",
" Critic_Count User_Score User_Count Developer Rating \n",
"0 51.0 8 322.0 Nintendo E \n",
"1 NaN NaN NaN NaN NaN \n",
"2 73.0 8.3 709.0 Nintendo E \n",
"3 73.0 8 192.0 Nintendo E \n",
"4 NaN NaN NaN NaN NaN \n",
" Name Platform Year_of_Release Genre Publisher \\\n",
"0 Wii Sports Wii 2006.0 Sports Nintendo \n",
"1 Super Mario Bros. NES 1985.0 Platform Nintendo \n",
"2 Mario Kart Wii Wii 2008.0 Racing Nintendo \n",
"3 Wii Sports Resort Wii 2009.0 Sports Nintendo \n",
"4 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing Nintendo \n",
"5 Tetris GB 1989.0 Puzzle Nintendo \n",
"6 New Super Mario Bros. DS 2006.0 Platform Nintendo \n",
"7 Wii Play Wii 2006.0 Misc Nintendo \n",
"\n",
" NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score \\\n",
"0 41.36 28.96 3.77 8.45 82.53 76.0 \n",
"1 29.08 3.58 6.81 0.77 40.24 NaN \n",
"2 15.68 12.76 3.79 3.29 35.52 82.0 \n",
"3 15.61 10.93 3.28 2.95 32.77 80.0 \n",
"4 11.27 8.89 10.22 1.00 31.37 NaN \n",
"5 23.20 2.26 4.22 0.58 30.26 NaN \n",
"6 11.28 9.14 6.50 2.88 29.80 89.0 \n",
"7 13.96 9.18 2.93 2.84 28.92 58.0 \n",
"\n",
" Critic_Count User_Score User_Count Developer Rating \n",
"0 51.0 8 322.0 Nintendo E \n",
"1 NaN NaN NaN NaN NaN \n",
"2 73.0 8.3 709.0 Nintendo E \n",
"3 73.0 8 192.0 Nintendo E \n",
"4 NaN NaN NaN NaN NaN \n",
"5 NaN NaN NaN NaN NaN \n",
"6 65.0 8.5 431.0 Nintendo E \n",
"7 41.0 6.6 129.0 Nintendo E \n"
]
}
],
"source": [
"print('----------')\n",
"print('#head')\n",
"print(videoReview.head())\n",
"print(videoReview.head(8))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"#tail\n",
" Name Platform Year_of_Release Genre \\\n",
"16714 Samurai Warriors: Sanada Maru PS3 2016.0 Action \n",
"16715 LMA Manager 2007 X360 2006.0 Sports \n",
"16716 Haitaka no Psychedelica PSV 2016.0 Adventure \n",
"16717 Spirits & Spells GBA 2003.0 Platform \n",
"16718 Winning Post 8 2016 PSV 2016.0 Simulation \n",
"\n",
" Publisher NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales \\\n",
"16714 Tecmo Koei 0.00 0.00 0.01 0.0 0.01 \n",
"16715 Codemasters 0.00 0.01 0.00 0.0 0.01 \n",
"16716 Idea Factory 0.00 0.00 0.01 0.0 0.01 \n",
"16717 Wanadoo 0.01 0.00 0.00 0.0 0.01 \n",
"16718 Tecmo Koei 0.00 0.00 0.01 0.0 0.01 \n",
"\n",
" Critic_Score Critic_Count User_Score User_Count Developer Rating \n",
"16714 NaN NaN NaN NaN NaN NaN \n",
"16715 NaN NaN NaN NaN NaN NaN \n",
"16716 NaN NaN NaN NaN NaN NaN \n",
"16717 NaN NaN NaN NaN NaN NaN \n",
"16718 NaN NaN NaN NaN NaN NaN \n",
" Name Platform \\\n",
"16711 Aiyoku no Eustia PSV \n",
"16712 Woody Woodpecker in Crazy Castle 5 GBA \n",
"16713 SCORE International Baja 1000: The Official Game PS2 \n",
"16714 Samurai Warriors: Sanada Maru PS3 \n",
"16715 LMA Manager 2007 X360 \n",
"16716 Haitaka no Psychedelica PSV \n",
"16717 Spirits & Spells GBA \n",
"16718 Winning Post 8 2016 PSV \n",
"\n",
" Year_of_Release Genre Publisher NA_Sales EU_Sales \\\n",
"16711 2014.0 Misc dramatic create 0.00 0.00 \n",
"16712 2002.0 Platform Kemco 0.01 0.00 \n",
"16713 2008.0 Racing Activision 0.00 0.00 \n",
"16714 2016.0 Action Tecmo Koei 0.00 0.00 \n",
"16715 2006.0 Sports Codemasters 0.00 0.01 \n",
"16716 2016.0 Adventure Idea Factory 0.00 0.00 \n",
"16717 2003.0 Platform Wanadoo 0.01 0.00 \n",
"16718 2016.0 Simulation Tecmo Koei 0.00 0.00 \n",
"\n",
" JP_Sales Other_Sales Global_Sales Critic_Score Critic_Count \\\n",
"16711 0.01 0.0 0.01 NaN NaN \n",
"16712 0.00 0.0 0.01 NaN NaN \n",
"16713 0.00 0.0 0.01 NaN NaN \n",
"16714 0.01 0.0 0.01 NaN NaN \n",
"16715 0.00 0.0 0.01 NaN NaN \n",
"16716 0.01 0.0 0.01 NaN NaN \n",
"16717 0.00 0.0 0.01 NaN NaN \n",
"16718 0.01 0.0 0.01 NaN NaN \n",
"\n",
" User_Score User_Count Developer Rating \n",
"16711 NaN NaN NaN NaN \n",
"16712 NaN NaN NaN NaN \n",
"16713 NaN NaN NaN NaN \n",
"16714 NaN NaN NaN NaN \n",
"16715 NaN NaN NaN NaN \n",
"16716 NaN NaN NaN NaN \n",
"16717 NaN NaN NaN NaN \n",
"16718 NaN NaN NaN NaN \n"
]
}
],
"source": [
"print('----------')\n",
"print('#tail')\n",
"print(videoReview.tail())\n",
"print(videoReview.tail(8))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"#Describe\n",
" Year_of_Release NA_Sales EU_Sales JP_Sales \\\n",
"count 16450.000000 16719.000000 16719.000000 16719.000000 \n",
"mean 2006.487356 0.263330 0.145025 0.077602 \n",
"std 5.878995 0.813514 0.503283 0.308818 \n",
"min 1980.000000 0.000000 0.000000 0.000000 \n",
"25% 2003.000000 0.000000 0.000000 0.000000 \n",
"50% 2007.000000 0.080000 0.020000 0.000000 \n",
"75% 2010.000000 0.240000 0.110000 0.040000 \n",
"max 2020.000000 41.360000 28.960000 10.220000 \n",
"\n",
" Other_Sales Global_Sales Critic_Score Critic_Count User_Count \n",
"count 16719.000000 16719.000000 8137.000000 8137.000000 7590.000000 \n",
"mean 0.047332 0.533543 68.967679 26.360821 162.229908 \n",
"std 0.186710 1.547935 13.938165 18.980495 561.282326 \n",
"min 0.000000 0.010000 13.000000 3.000000 4.000000 \n",
"25% 0.000000 0.060000 60.000000 12.000000 10.000000 \n",
"50% 0.010000 0.170000 71.000000 21.000000 24.000000 \n",
"75% 0.030000 0.470000 79.000000 36.000000 81.000000 \n",
"max 10.570000 82.530000 98.000000 113.000000 10665.000000 \n",
"count 16719\n",
"unique 31\n",
"top PS2\n",
"freq 2161\n",
"Name: Platform, dtype: object\n",
"count 16450.000000\n",
"mean 2006.487356\n",
"std 5.878995\n",
"min 1980.000000\n",
"25% 2003.000000\n",
"50% 2007.000000\n",
"75% 2010.000000\n",
"max 2020.000000\n",
"Name: Year_of_Release, dtype: float64\n"
]
}
],
"source": [
"print('----------')\n",
"print('#Describe')\n",
"print(videoReview.describe()) # calculates measures of central tendency\n",
"print(videoReview['Platform'].describe())\n",
"print(videoReview['Year_of_Release'].describe())\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"#info\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 16719 entries, 0 to 16718\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Name 16717 non-null object \n",
" 1 Platform 16719 non-null object \n",
" 2 Year_of_Release 16450 non-null float64\n",
" 3 Genre 16717 non-null object \n",
" 4 Publisher 16665 non-null object \n",
" 5 NA_Sales 16719 non-null float64\n",
" 6 EU_Sales 16719 non-null float64\n",
" 7 JP_Sales 16719 non-null float64\n",
" 8 Other_Sales 16719 non-null float64\n",
" 9 Global_Sales 16719 non-null float64\n",
" 10 Critic_Score 8137 non-null float64\n",
" 11 Critic_Count 8137 non-null float64\n",
" 12 User_Score 10015 non-null object \n",
" 13 User_Count 7590 non-null float64\n",
" 14 Developer 10096 non-null object \n",
" 15 Rating 9950 non-null object \n",
"dtypes: float64(9), object(7)\n",
"memory usage: 2.0+ MB\n",
"None\n"
]
}
],
"source": [
"print('----------')\n",
"print('#info')\n",
"print(videoReview.info()) # memory footprint and datatypes"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Transpose - Describe\n",
" count mean std min 25% 50% \\\n",
"Year_of_Release 16450.0 2006.487356 5.878995 1980.00 2003.00 2007.00 \n",
"NA_Sales 16719.0 0.263330 0.813514 0.00 0.00 0.08 \n",
"EU_Sales 16719.0 0.145025 0.503283 0.00 0.00 0.02 \n",
"JP_Sales 16719.0 0.077602 0.308818 0.00 0.00 0.00 \n",
"Other_Sales 16719.0 0.047332 0.186710 0.00 0.00 0.01 \n",
"Global_Sales 16719.0 0.533543 1.547935 0.01 0.06 0.17 \n",
"Critic_Score 8137.0 68.967679 13.938165 13.00 60.00 71.00 \n",
"Critic_Count 8137.0 26.360821 18.980495 3.00 12.00 21.00 \n",
"User_Count 7590.0 162.229908 561.282326 4.00 10.00 24.00 \n",
"\n",
" 75% max \n",
"Year_of_Release 2010.00 2020.00 \n",
"NA_Sales 0.24 41.36 \n",
"EU_Sales 0.11 28.96 \n",
"JP_Sales 0.04 10.22 \n",
"Other_Sales 0.03 10.57 \n",
"Global_Sales 0.47 82.53 \n",
"Critic_Score 79.00 98.00 \n",
"Critic_Count 36.00 113.00 \n",
"User_Count 81.00 10665.00 \n"
]
}
],
"source": [
"print('----------')\n",
"print('Transpose - Describe')\n",
"print(videoReview.describe().transpose())\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Iterate some rows from DF\n",
"#### Printing row ####\n",
"Wii Sports\n",
"#### Printing row ####\n",
"Super Mario Bros.\n",
"#### Printing row ####\n",
"Mario Kart Wii\n",
"#### Printing row ####\n",
"Wii Sports Resort\n",
"#### Printing row ####\n",
"Pokemon Red/Pokemon Blue\n",
"#### Printing row ####\n",
"Tetris\n",
"#### Printing row ####\n",
"New Super Mario Bros.\n",
"#### Printing row ####\n",
"Wii Play\n",
"#### Printing row ####\n",
"New Super Mario Bros. Wii\n"
]
}
],
"source": [
"print('----------')\n",
"print('Iterate some rows from DF')\n",
"for i, row in videoReview[:9].iterrows():\n",
" print('#### Printing row ####')\n",
" print(row['Name'])\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Group by Year, Platform by Count : \n",
" Name Genre Publisher NA_Sales EU_Sales \\\n",
"Year_of_Release Platform \n",
"1980.0 2600 9 9 9 9 9 \n",
"1981.0 2600 46 46 46 46 46 \n",
"1982.0 2600 36 36 36 36 36 \n",
"1983.0 2600 11 11 11 11 11 \n",
" NES 6 6 6 6 6 \n",
"... ... ... ... ... ... \n",
"2016.0 X360 13 13 13 13 13 \n",
" XOne 87 87 87 87 87 \n",
"2017.0 PS4 1 1 1 1 1 \n",
" PSV 2 2 2 2 2 \n",
"2020.0 DS 1 1 1 1 1 \n",
"\n",
" JP_Sales Other_Sales Global_Sales Critic_Score \\\n",
"Year_of_Release Platform \n",
"1980.0 2600 9 9 9 0 \n",
"1981.0 2600 46 46 46 0 \n",
"1982.0 2600 36 36 36 0 \n",
"1983.0 2600 11 11 11 0 \n",
" NES 6 6 6 0 \n",
"... ... ... ... ... \n",
"2016.0 X360 13 13 13 0 \n",
" XOne 87 87 87 60 \n",
"2017.0 PS4 1 1 1 0 \n",
" PSV 2 2 2 0 \n",
"2020.0 DS 1 1 1 0 \n",
"\n",
" Critic_Count User_Score User_Count Developer \\\n",
"Year_of_Release Platform \n",
"1980.0 2600 0 0 0 0 \n",
"1981.0 2600 0 0 0 0 \n",
"1982.0 2600 0 0 0 0 \n",
"1983.0 2600 0 0 0 0 \n",
" NES 0 0 0 0 \n",
"... ... ... ... ... \n",
"2016.0 X360 0 12 7 12 \n",
" XOne 60 74 66 74 \n",
"2017.0 PS4 0 0 0 0 \n",
" PSV 0 0 0 0 \n",
"2020.0 DS 0 1 0 1 \n",
"\n",
" Rating \n",
"Year_of_Release Platform \n",
"1980.0 2600 0 \n",
"1981.0 2600 0 \n",
"1982.0 2600 0 \n",
"1983.0 2600 0 \n",
" NES 0 \n",
"... ... \n",
"2016.0 X360 12 \n",
" XOne 71 \n",
"2017.0 PS4 0 \n",
" PSV 0 \n",
"2020.0 DS 1 \n",
"\n",
"[241 rows x 14 columns]\n"
]
}
],
"source": [
"#Subsetting and ordering Pandas\n",
"print('----------')\n",
"print('Group by Year, Platform by Count : ')\n",
"print(videoReview.groupby(['Year_of_Release','Platform']).count())\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Group by : for Year=2016 group by Platform and count Global Sales\n",
"Platform\n",
"3DS 15.14\n",
"PC 5.27\n",
"PS3 3.58\n",
"PS4 69.29\n",
"PSV 4.27\n",
"Wii 0.18\n",
"WiiU 4.58\n",
"X360 1.52\n",
"XOne 26.27\n",
"Name: Global_Sales, dtype: float64\n"
]
}
],
"source": [
"print('Group by : for Year=2016 group by Platform and count Global Sales')\n",
"print(videoReview[videoReview.Year_of_Release==2016.0].groupby('Platform')['Global_Sales'].sum())"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Sorting and Ordering\n",
" Name Platform Year_of_Release Genre Publisher \\\n",
"15 Wii Fit Plus Wii 2009.0 Sports Nintendo \n",
"8 New Super Mario Bros. Wii Wii 2009.0 Platform Nintendo \n",
"7 Wii Play Wii 2006.0 Misc Nintendo \n",
"3 Wii Sports Resort Wii 2009.0 Sports Nintendo \n",
"2 Mario Kart Wii Wii 2008.0 Racing Nintendo \n",
"0 Wii Sports Wii 2006.0 Sports Nintendo \n",
"\n",
" NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score \\\n",
"15 9.01 8.49 2.53 1.77 21.79 80.0 \n",
"8 14.44 6.94 4.70 2.24 28.32 87.0 \n",
"7 13.96 9.18 2.93 2.84 28.92 58.0 \n",
"3 15.61 10.93 3.28 2.95 32.77 80.0 \n",
"2 15.68 12.76 3.79 3.29 35.52 82.0 \n",
"0 41.36 28.96 3.77 8.45 82.53 76.0 \n",
"\n",
" Critic_Count User_Score User_Count Developer Rating \n",
"15 33.0 7.4 52.0 Nintendo E \n",
"8 80.0 8.4 594.0 Nintendo E \n",
"7 41.0 6.6 129.0 Nintendo E \n",
"3 73.0 8 192.0 Nintendo E \n",
"2 73.0 8.3 709.0 Nintendo E \n",
"0 51.0 8 322.0 Nintendo E \n"
]
}
],
"source": [
"#More Panda functions - Sorting & Ordering\n",
"print('----------')\n",
"print('Sorting and Ordering')\n",
"df = videoReview[(videoReview.Platform=='Wii') & (videoReview.NA_Sales>9)]\n",
"print(df.sort_values('Global_Sales', ascending=True))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"#Writing a Panda to a CSV file\n",
"df.to_csv('/Users/Rafael/video_games_wii.csv', sep=',')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Unnamed: 0 Name Platform Year_of_Release Genre \\\n",
"0 0 Wii Sports Wii 2006.0 Sports \n",
"1 2 Mario Kart Wii Wii 2008.0 Racing \n",
"2 3 Wii Sports Resort Wii 2009.0 Sports \n",
"3 7 Wii Play Wii 2006.0 Misc \n",
"4 8 New Super Mario Bros. Wii Wii 2009.0 Platform \n",
"\n",
" Publisher NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales \\\n",
"0 Nintendo 41.36 28.96 3.77 8.45 82.53 \n",
"1 Nintendo 15.68 12.76 3.79 3.29 35.52 \n",
"2 Nintendo 15.61 10.93 3.28 2.95 32.77 \n",
"3 Nintendo 13.96 9.18 2.93 2.84 28.92 \n",
"4 Nintendo 14.44 6.94 4.70 2.24 28.32 \n",
"\n",
" Critic_Score Critic_Count User_Score User_Count Developer Rating \n",
"0 76.0 51.0 8.0 322.0 Nintendo E \n",
"1 82.0 73.0 8.3 709.0 Nintendo E \n",
"2 80.0 73.0 8.0 192.0 Nintendo E \n",
"3 58.0 41.0 6.6 129.0 Nintendo E \n",
"4 87.0 80.0 8.4 594.0 Nintendo E \n"
]
}
],
"source": [
"#Go inspect the CSV file created.\n",
"#Is it what you expected?\n",
"#Could the output be formatted differently?\n",
"#If so, look up the Pandas 'to_csv' function to see what you can change\n",
"df_loaded = pd.read_csv('/Users/Rafael/video_games_wii.csv', sep=',');\n",
"print(df_loaded.head())"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'Get' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[41], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m Get\u001b[38;5;241m-\u001b[39mContent \u001b[38;5;241m/\u001b[39mUsers\u001b[38;5;241m/\u001b[39mRafael\u001b[38;5;241m/\u001b[39mvideo_games_wii\u001b[38;5;241m.\u001b[39mcsv\n",
"\u001b[1;31mNameError\u001b[0m: name 'Get' is not defined"
]
}
],
"source": [
"Get-Content /Users/Rafael/video_games_wii.csv"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv('/Users/Rafael/video_games_wii.csv', sep=',', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------\n",
"Plotting - Histogram\n"
]
},
{
"data": {
"text/plain": [
"<Axes: ylabel='Frequency'>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Creating Graphs for a Panda\n",
"#plotting\n",
"print('----------')\n",
"print('Plotting - Histogram')\n",
"videoReview['Year_of_Release'].plot(kind='hist')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#If the chart does not appear in the above cell, just go back to that cell and rerun. It should appear now."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Can you create any other plots?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.12.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|