Category comparison - pandas
I have the following dataframe:
{
'views_per_day': {0: 37, 1: 26, 2: 10, 3: 17, 4: 5, 5: 13, 6: 5, 7: 15, 8: 7, 9: 7, 10: 3, 11: 13, 12: 9, 13: 27, 14: 13, 15: 6, 16: 17, 17: 12, 18: 9, 19: 16, 20: 6, 21: 11, 22: 5, 23: 12, 24: 10, 25: 4, 26: 12, 27: 8, 28: 9, 29: 8, 30: 11, 31: 18, 32: 15, 33: 4, 34: 8, 35: 7, 36: 8, 37: 8, 38: 5, 39: 8, 40: 10, 41: 33, 42: 8, 43: 7, 44: 7, 45: 7, 46: 7, 47: 7, 48: 6, 49: 7, 50: 6, 51: 5, 52: 3, 53: 6, 54: 13, 55: 6, 56: 6, 57: 7, 58: 6, 59: 5, 60: 4, 61: 5, 62: 6, 63: 6, 64: 3, 65: 6, 66: 6, 67: 4, 68: 5, 69: 4, 70: 6, 71: 5, 72: 5, 73: 3, 74: 4, 75: 5, 76: 8, 77: 30, 78: 9, 79: 5, 80: 5, 81: 6, 82: 5, 83: 4, 84: 7, 85: 4, 86: 4, 87: 5, 88: 3, 89: 3, 90: 7, 91: 3, 92: 4, 93: 8, 94: 2, 95: 3, 96: 5, 97: 4, 98: 4, 99: 4, 100: 4, 101: 4, 102: 4, 103: 3, 104: 4, 105: 6, 106: 3, 107: 3, 108: 3, 109: 3, 110: 3, 111: 3, 112: 4, 113: 3, 114: 3, 115: 4, 116: 3, 117: 3, 118: 2, 119: 3, 120: 3, 121: 2, 122: 4, 123: 4, 124: 4, 125: 26, 126: 3, 127: 3, 128: 5, 129: 3, 130: 2, 131: 4, 132: 3, 133: 4, 134: 3, 135: 4, 136: 3, 137: 3, 138: 5, 139: 2, 140: 5, 141: 3, 142: 3, 143: 4, 144: 3, 145: 3, 146: 4, 147: 4, 148: 7, 149: 3, 150: 3, 151: 3, 152: 2, 153: 3, 154: 12, 155: 3, 156: 2, 157: 3, 158: 2, 159: 2, 160: 3, 161: 4, 162: 2, 163: 2, 164: 4, 165: 3, 166: 3, 167: 5, 168: 1, 169: 2, 170: 11, 171: 3, 172: 2, 173: 4, 174: 4, 175: 2, 176: 6, 177: 2, 178: 4, 179: 2, 180: 3, 181: 2, 182: 2, 183: 2, 184: 2, 185: 2, 186: 2, 187: 2, 188: 3, 189: 2, 190: 3, 191: 2, 192: 2, 193: 2, 194: 3, 195: 3, 196: 2, 197: 2, 198: 2, 199: 3, 200: 2, 201: 2, 202: 2, 203: 2, 204: 3, 205: 2, 206: 2, 207: 2, 208: 1, 209: 5, 210: 2, 211: 2, 212: 2, 213: 5, 214: 3, 215: 3, 216: 1, 217: 2, 218: 2, 219: 1, 220: 2, 221: 2, 222: 3, 223: 3, 224: 2, 225: 1, 226: 4, 227: 1, 228: 1, 229: 2, 230: 2, 231: 4, 232: 1, 233: 2, 234: 1, 235: 1, 236: 2, 237: 2, 238: 2, 239: 8, 240: 2, 241: 2, 242: 1, 243: 2, 244: 1, 245: 1, 246: 2, 247: 1, 248: 1, 249: 2, 250: 3, 251: 4, 252: 4, 253: 1, 254: 41, 255: 28, 256: 59, 257: 35, 258: 11, 259: 12, 260: 12, 261: 8, 262: 6, 263: 8, 264: 10, 265: 15, 266: 15, 267: 5, 268: 8, 269: 15, 270: 11, 271: 16, 272: 11, 273: 12, 274: 11, 275: 5, 276: 9, 277: 10, 278: 20, 279: 5, 280: 11, 281: 5, 282: 7, 283: 9, 284: 9, 285: 9, 286: 9, 287: 4, 288: 9, 289: 4, 290: 6, 291: 4, 292: 8, 293: 3, 294: 9, 295: 9, 296: 9, 297: 5, 298: 4, 299: 10, 300: 18, 301: 4, 302: 5, 303: 5, 304: 11, 305: 9, 306: 7, 307: 4, 308: 7, 309: 9, 310: 5, 311: 4, 312: 8, 313: 3, 314: 7, 315: 12, 316: 3, 317: 6, 318: 6, 319: 6, 320: 6, 321: 7, 322: 6, 323: 6, 324: 8, 325: 6, 326: 3, 327: 6, 328: 4, 329: 4, 330: 4, 331: 7, 332: 6, 333: 10, 334: 3, 335: 5, 336: 6, 337: 13, 338: 12, 339: 3, 340: 5, 341: 5, 342: 4, 343: 5, 344: 3, 345: 3, 346: 5, 347: 4, 348: 5, 349: 2, 350: 5, 351: 5, 352: 13, 353: 10, 354: 7, 355: 2, 356: 3, 357: 5, 358: 4, 359: 7, 360: 4, 361: 4, 362: 4, 363: 6, 364: 5, 365: 4, 366: 4, 367: 6, 368: 6, 369: 5, 370: 4, 371: 4, 372: 2, 373: 3, 374: 4, 375: 4, 376: 4, 377: 4, 378: 4, 379: 4, 380: 4, 381: 2, 382: 3, 383: 4, 384: 2, 385: 4, 386: 4, 387: 4, 388: 5, 389: 3, 390: 3, 391: 4, 392: 3, 393: 3, 394: 3, 395: 2, 396: 4, 397: 3, 398: 2, 399: 3, 400: 2, 401: 4, 402: 3, 403: 3, 404: 4, 405: 3, 406: 4, 407: 4, 408: 5, 409: 3, 410: 6, 411: 3, 412: 3, 413: 2, 414: 3, 415: 3, 416: 3, 417: 3, 418: 1, 419: 2, 420: 5, 421: 4, 422: 3, 423: 2, 424: 3, 425: 3, 426: 3, 427: 2, 428: 3, 429: 3, 430: 3, 431: 3, 432: 2, 433: 3, 434: 3, 435: 2, 436: 2, 437: 3, 438: 2, 439: 2, 440: 4, 441: 16, 442: 2, 443: 2, 444: 2, 445: 2, 446: 2, 447: 2, 448: 2, 449: 2, 450: 2, 451: 3, 452: 3, 453: 2, 454: 2, 455: 2, 456: 4, 457: 2, 458: 3, 459: 2, 460: 2, 461: 2, 462: 2, 463: 1, 464: 3, 465: 3, 466: 2, 467: 2, 468: 18, 469: 2, 470: 2, 471: 2, 472: 5, 473: 2, 474: 3, 475: 2, 476: 2, 477: 2, 478: 2, 479: 2, 480: 1, 481: 4, 482: 2, 483: 2, 484: 2, 485: 2, 486: 3, 487: 2, 488: 2, 489: 6, 490: 1, 491: 3, 492: 2, 493: 1, 494: 2, 495: 3, 496: 3, 497: 2, 498: 1, 499: 2, 500: 1, 501: 1, 502: 1, 503: 1, 504: 1, 505: 1, 506: 3, 507: 4, 508: 2, 509: 2, 510: 1, 511: 1, 512: 2, 513: 2, 514: 2, 515: 2, 516: 1, 517: 1, 518: 1, 519: 1, 520: 1, 521: 3, 522: 11, 523: 2, 524: 1, 525: 48, 526: 51, 527: 20, 528: 26, 529: 23, 530: 8, 531: 6, 532: 17, 533: 17, 534: 15, 535: 12, 536: 17, 537: 14, 538: 12, 539: 18, 540: 11, 541: 14, 542: 10, 543: 7, 544: 13, 545: 10, 546: 10, 547: 5, 548: 8, 549: 7, 550: 7, 551: 9, 552: 5, 553: 8, 554: 19, 555: 5, 556: 14, 557: 11, 558: 7, 559: 5, 560: 6, 561: 8, 562: 8, 563: 12, 564: 8, 565: 7, 566: 4, 567: 7, 568: 8, 569: 9, 570: 4, 571: 6, 572: 7, 573: 14, 574: 5, 575: 4, 576: 6, 577: 8, 578: 6, 579: 6, 580: 3, 581: 3, 582: 6, 583: 3, 584: 3, 585: 7, 586: 6, 587: 4, 588: 11, 589: 6, 590: 4, 591: 5, 592: 5, 593: 4, 594: 7, 595: 5, 596: 5, 597: 3, 598: 7, 599: 5, 600: 5, 601: 4, 602: 5, 603: 4, 604: 4, 605: 5, 606: 5, 607: 5, 608: 4, 609: 3, 610: 6, 611: 4, 612: 5, 613: 4, 614: 4, 615: 4, 616: 4, 617: 5, 618: 4, 619: 4, 620: 5, 621: 4, 622: 4, 623: 2, 624: 4, 625: 4, 626: 3, 627: 7, 628: 2, 629: 4, 630: 4, 631: 7, 632: 3, 633: 4, 634: 4, 635: 3, 636: 3, 637: 3, 638: 4, 639: 3, 640: 3, 641: 2, 642: 3, 643: 2, 644: 3, 645: 3, 646: 3, 647: 3, 648: 3, 649: 3, 650: 3, 651: 4, 652: 2, 653: 5, 654: 4, 655: 4, 656: 5, 657: 4, 658: 3, 659: 3, 660: 2, 661: 6, 662: 2, 663: 4, 664: 3, 665: 3, 666: 2, 667: 4, 668: 4, 669: 7, 670: 3, 671: 2, 672: 2, 673: 6, 674: 2, 675: 2, 676: 2, 677: 2, 678: 2, 679: 4, 680: 3, 681: 3, 682: 2, 683: 3, 684: 3, 685: 2, 686: 2, 687: 3, 688: 3, 689: 2, 690: 2, 691: 2, 692: 2, 693: 2, 694: 2, 695: 2, 696: 4, 697: 2, 698: 5, 699: 2, 700: 2, 701: 3, 702: 2, 703: 2, 704: 3, 705: 2, 706: 3, 707: 2, 708: 3, 709: 2, 710: 2, 711: 2, 712: 2, 713: 2, 714: 2, 715: 2, 716: 2, 717: 2, 718: 2, 719: 2, 720: 3, 721: 3, 722: 2, 723: 2, 724: 2, 725: 2, 726: 3, 727: 2, 728: 2, 729: 2, 730: 2, 731: 2, 732: 2, 733: 2, 734: 2, 735: 2, 736: 2, 737: 2, 738: 2, 739: 2, 740: 2, 741: 2, 742: 2, 743: 1, 744: 2, 745: 2, 746: 2, 747: 2, 748: 2, 749: 2, 750: 2, 751: 2, 752: 1, 753: 4, 754: 4, 755: 1, 756: 1, 757: 1, 758: 3, 759: 5, 760: 2, 761: 2, 762: 1, 763: 2, 764: 2, 765: 2, 766: 1, 767: 2, 768: 1, 769: 1, 770: 1, 771: 2, 772: 1, 773: 1, 774: 1, 775: 1, 776: 1, 777: 1, 778: 1},
'status': {0: 'Extra', 1: 'Extra', 2: 'Extra', 3: 'Standard', 4: 'Extra', 5: 'Extra', 6: 'Extra', 7: 'Extra', 8: 'Standard', 9: 'Extra', 10: 'Extra', 11: 'Extra', 12: 'Extra', 13: 'Extra', 14: 'Standard', 15: 'Extra', 16: 'Extra', 17: 'Extra', 18: 'Extra', 19: 'Extra', 20: 'Extra', 21: 'Standard', 22: 'Standard', 23: 'Extra', 24: 'Standard', 25: 'Extra', 26: 'Extra', 27: 'Standard', 28: 'Extra', 29: 'Standard', 30: 'Extra', 31: 'Standard', 32: 'Extra', 33: 'Extra', 34: 'Standard', 35: 'Standard', 36: 'Standard', 37: 'Standard', 38: 'Standard', 39: 'Standard', 40: 'Extra', 41: 'Standard', 42: 'Extra', 43: 'Standard', 44: 'Standard', 45: 'Extra', 46: 'Standard', 47: 'Standard', 48: 'Standard', 49: 'Standard', 50: 'Standard', 51: 'Standard', 52: 'Standard', 53: 'Extra', 54: 'Standard', 55: 'Extra', 56: 'Standard', 57: 'Extra', 58: 'Standard', 59: 'Standard', 60: 'Extra', 61: 'Standard', 62: 'Extra', 63: 'Standard', 64: 'Extra', 65: 'Standard', 66: 'Standard', 67: 'Extra', 68: 'Standard', 69: 'Standard', 70: 'Extra', 71: 'Standard', 72: 'Extra', 73: 'Standard', 74: 'Standard', 75: 'Standard', 76: 'Standard', 77: 'Standard', 78: 'Standard', 79: 'Standard', 80: 'Standard', 81: 'Standard', 82: 'Standard', 83: 'Standard', 84: 'Standard', 85: 'Standard', 86: 'Standard', 87: 'Standard', 88: 'Standard', 89: 'Extra', 90: 'Extra', 91: 'Standard', 92: 'Extra', 93: 'Standard', 94: 'Standard', 95: 'Standard', 96: 'Standard', 97: 'Extra', 98: 'Standard', 99: 'Standard', 100: 'Standard', 101: 'Standard', 102: 'Standard', 103: 'Standard', 104: 'Standard', 105: 'Standard', 106: 'Standard', 107: 'Standard', 108: 'Standard', 109: 'Standard', 110: 'Standard', 111: 'Extra', 112: 'Standard', 113: 'Extra', 114: 'Standard', 115: 'Extra', 116: 'Standard', 117: 'Standard', 118: 'Extra', 119: 'Standard', 120: 'Standard', 121: 'Standard', 122: 'Standard', 123: 'Standard', 124: 'Extra', 125: 'Standard', 126: 'Standard', 127: 'Standard', 128: 'Standard', 129: 'Standard', 130: 'Extra', 131: 'Standard', 132: 'Standard', 133: 'Standard', 134: 'Standard', 135: 'Standard', 136: 'Standard', 137: 'Standard', 138: 'Standard', 139: 'Extra', 140: 'Standard', 141: 'Standard', 142: 'Standard', 143: 'Standard', 144: 'Standard', 145: 'Standard', 146: 'Standard', 147: 'Standard', 148: 'Standard', 149: 'Standard', 150: 'Extra', 151: 'Standard', 152: 'Standard', 153: 'Standard', 154: 'Extra', 155: 'Standard', 156: 'Extra', 157: 'Extra', 158: 'Standard', 159: 'Standard', 160: 'Standard', 161: 'Standard', 162: 'Standard', 163: 'Extra', 164: 'Standard', 165: 'Standard', 166: 'Extra', 167: 'Standard', 168: 'Standard', 169: 'Standard', 170: 'Standard', 171: 'Standard', 172: 'Standard', 173: 'Standard', 174: 'Standard', 175: 'Standard', 176: 'Standard', 177: 'Standard', 178: 'Standard', 179: 'Standard', 180: 'Standard', 181: 'Standard', 182: 'Standard', 183: 'Standard', 184: 'Standard', 185: 'Standard', 186: 'Standard', 187: 'Standard', 188: 'Standard', 189: 'Standard', 190: 'Standard', 191: 'Standard', 192: 'Standard', 193: 'Standard', 194: 'Standard', 195: 'Standard', 196: 'Standard', 197: 'Standard', 198: 'Standard', 199: 'Standard', 200: 'Standard', 201: 'Standard', 202: 'Standard', 203: 'Standard', 204: 'Standard', 205: 'Standard', 206: 'Standard', 207: 'Standard', 208: 'Standard', 209: 'Standard', 210: 'Standard', 211: 'Extra', 212: 'Standard', 213: 'Standard', 214: 'Standard', 215: 'Standard', 216: 'Standard', 217: 'Standard', 218: 'Standard', 219: 'Standard', 220: 'Standard', 221: 'Standard', 222: 'Standard', 223: 'Standard', 224: 'Standard', 225: 'Standard', 226: 'Standard', 227: 'Standard', 228: 'Standard', 229: 'Standard', 230: 'Standard', 231: 'Standard', 232: 'Standard', 233: 'Standard', 234: 'Standard', 235: 'Standard', 236: 'Standard', 237: 'Standard', 238: 'Standard', 239: 'Standard', 240: 'Standard', 241: 'Standard', 242: 'Standard', 243: 'Standard', 244: 'Standard', 245: 'Standard', 246: 'Standard', 247: 'Extra', 248: 'Standard', 249: 'Standard', 250: 'Standard', 251: 'Extra', 252: 'Extra', 253: 'Standard', 254: 'Extra', 255: 'Extra', 256: 'Extra', 257: 'Extra', 258: 'Extra', 259: 'Extra', 260: 'Extra', 261: 'Extra', 262: 'Extra', 263: 'Standard', 264: 'Extra', 265: 'Standard', 266: 'Extra', 267: 'Extra', 268: 'Standard', 269: 'Standard', 270: 'Extra', 271: 'Extra', 272: 'Extra', 273: 'Standard', 274: 'Extra', 275: 'Standard', 276: 'Extra', 277: 'Standard', 278: 'Standard', 279: 'Standard', 280: 'Standard', 281: 'Standard', 282: 'Standard', 283: 'Standard', 284: 'Extra', 285: 'Extra', 286: 'Standard', 287: 'Standard', 288: 'Extra', 289: 'Standard', 290: 'Standard', 291: 'Extra', 292: 'Extra', 293: 'Extra', 294: 'Extra', 295: 'Standard', 296: 'Standard', 297: 'Extra', 298: 'Extra', 299: 'Standard', 300: 'Standard', 301: 'Extra', 302: 'Standard', 303: 'Standard', 304: 'Standard', 305: 'Standard', 306: 'Standard', 307: 'Extra', 308: 'Standard', 309: 'Standard', 310: 'Standard', 311: 'Extra', 312: 'Extra', 313: 'Extra', 314: 'Standard', 315: 'Standard', 316: 'Standard', 317: 'Extra', 318: 'Standard', 319: 'Extra', 320: 'Standard', 321: 'Extra', 322: 'Standard', 323: 'Extra', 324: 'Standard', 325: 'Extra', 326: 'Standard', 327: 'Standard', 328: 'Standard', 329: 'Extra', 330: 'Standard', 331: 'Standard', 332: 'Standard', 333: 'Standard', 334: 'Standard', 335: 'Extra', 336: 'Standard', 337: 'Standard', 338: 'Standard', 339: 'Extra', 340: 'Extra', 341: 'Extra', 342: 'Standard', 343: 'Standard', 344: 'Extra', 345: 'Standard', 346: 'Extra', 347: 'Standard', 348: 'Extra', 349: 'Standard', 350: 'Extra', 351: 'Standard', 352: 'Standard', 353: 'Standard', 354: 'Standard', 355: 'Standard', 356: 'Standard', 357: 'Standard', 358: 'Standard', 359: 'Standard', 360: 'Standard', 361: 'Standard', 362: 'Standard', 363: 'Extra', 364: 'Standard', 365: 'Standard', 366: 'Standard', 367: 'Extra', 368: 'Standard', 369: 'Standard', 370: 'Standard', 371: 'Extra', 372: 'Standard', 373: 'Standard', 374: 'Standard', 375: 'Standard', 376: 'Standard', 377: 'Standard', 378: 'Standard', 379: 'Extra', 380: 'Standard', 381: 'Standard', 382: 'Standard', 383: 'Extra', 384: 'Standard', 385: 'Standard', 386: 'Standard', 387: 'Standard', 388: 'Standard', 389: 'Standard', 390: 'Standard', 391: 'Standard', 392: 'Standard', 393: 'Standard', 394: 'Standard', 395: 'Standard', 396: 'Standard', 397: 'Standard', 398: 'Standard', 399: 'Extra', 400: 'Standard', 401: 'Standard', 402: 'Standard', 403: 'Standard', 404: 'Standard', 405: 'Standard', 406: 'Standard', 407: 'Standard', 408: 'Standard', 409: 'Extra', 410: 'Standard', 411: 'Standard', 412: 'Standard', 413: 'Standard', 414: 'Standard', 415: 'Standard', 416: 'Standard', 417: 'Standard', 418: 'Extra', 419: 'Standard', 420: 'Standard', 421: 'Standard', 422: 'Standard', 423: 'Standard', 424: 'Standard', 425: 'Standard', 426: 'Standard', 427: 'Standard', 428: 'Standard', 429: 'Standard', 430: 'Standard', 431: 'Standard', 432: 'Standard', 433: 'Extra', 434: 'Standard', 435: 'Standard', 436: 'Standard', 437: 'Standard', 438: 'Standard', 439: 'Standard', 440: 'Standard', 441: 'Standard', 442: 'Standard', 443: 'Standard', 444: 'Standard', 445: 'Standard', 446: 'Standard', 447: 'Standard', 448: 'Standard', 449: 'Standard', 450: 'Standard', 451: 'Standard', 452: 'Standard', 453: 'Standard', 454: 'Standard', 455: 'Standard', 456: 'Standard', 457: 'Standard', 458: 'Extra', 459: 'Extra', 460: 'Extra', 461: 'Standard', 462: 'Standard', 463: 'Standard', 464: 'Standard', 465: 'Standard', 466: 'Standard', 467: 'Standard', 468: 'Standard', 469: 'Standard', 470: 'Standard', 471: 'Extra', 472: 'Standard', 473: 'Standard', 474: 'Standard', 475: 'Standard', 476: 'Standard', 477: 'Extra', 478: 'Standard', 479: 'Standard', 480: 'Standard', 481: 'Standard', 482: 'Standard', 483: 'Standard', 484: 'Standard', 485: 'Standard', 486: 'Standard', 487: 'Standard', 488: 'Extra', 489: 'Standard', 490: 'Standard', 491: 'Standard', 492: 'Standard', 493: 'Standard', 494: 'Standard', 495: 'Standard', 496: 'Standard', 497: 'Standard', 498: 'Standard', 499: 'Extra', 500: 'Standard', 501: 'Standard', 502: 'Standard', 503: 'Standard', 504: 'Extra', 505: 'Standard', 506: 'Extra', 507: 'Standard', 508: 'Standard', 509: 'Standard', 510: 'Standard', 511: 'Extra', 512: 'Standard', 513: 'Standard', 514: 'Standard', 515: 'Extra', 516: 'Standard', 517: 'Standard', 518: 'Standard', 519: 'Extra', 520: 'Extra', 521: 'Standard', 522: 'Standard', 523: 'Standard', 524: 'Extra', 525: 'Extra', 526: 'Extra', 527: 'Standard', 528: 'Standard', 529: 'Extra', 530: 'Extra', 531: 'Extra', 532: 'Extra', 533: 'Extra', 534: 'Standard', 535: 'Extra', 536: 'Standard', 537: 'Standard', 538: 'Standard', 539: 'Extra', 540: 'Extra', 541: 'Extra', 542: 'Extra', 543: 'Extra', 544: 'Extra', 545: 'Standard', 546: 'Extra', 547: 'Extra', 548: 'Extra', 549: 'Standard', 550: 'Extra', 551: 'Extra', 552: 'Standard', 553: 'Standard', 554: 'Extra', 555: 'Extra', 556: 'Standard', 557: 'Extra', 558: 'Extra', 559: 'Extra', 560: 'Extra', 561: 'Extra', 562: 'Extra', 563: 'Standard', 564: 'Standard', 565: 'Extra', 566: 'Extra', 567: 'Extra', 568: 'Extra', 569: 'Standard', 570: 'Standard', 571: 'Extra', 572: 'Extra', 573: 'Standard', 574: 'Extra', 575: 'Standard', 576: 'Extra', 577: 'Extra', 578: 'Extra', 579: 'Extra', 580: 'Extra', 581: 'Standard', 582: 'Extra', 583: 'Standard', 584: 'Standard', 585: 'Extra', 586: 'Standard', 587: 'Standard', 588: 'Extra', 589: 'Standard', 590: 'Standard', 591: 'Extra', 592: 'Standard', 593: 'Standard', 594: 'Standard', 595: 'Extra', 596: 'Extra', 597: 'Extra', 598: 'Standard', 599: 'Standard', 600: 'Standard', 601: 'Standard', 602: 'Extra', 603: 'Extra', 604: 'Extra', 605: 'Standard', 606: 'Standard', 607: 'Standard', 608: 'Standard', 609: 'Standard', 610: 'Extra', 611: 'Standard', 612: 'Standard', 613: 'Standard', 614: 'Standard', 615: 'Standard', 616: 'Extra', 617: 'Standard', 618: 'Standard', 619: 'Standard', 620: 'Standard', 621: 'Standard', 622: 'Extra', 623: 'Standard', 624: 'Standard', 625: 'Standard', 626: 'Extra', 627: 'Standard', 628: 'Standard', 629: 'Standard', 630: 'Extra', 631: 'Extra', 632: 'Standard', 633: 'Extra', 634: 'Standard', 635: 'Standard', 636: 'Standard', 637: 'Standard', 638: 'Standard', 639: 'Standard', 640: 'Standard', 641: 'Standard', 642: 'Standard', 643: 'Standard', 644: 'Standard', 645: 'Standard', 646: 'Extra', 647: 'Extra', 648: 'Extra', 649: 'Standard', 650: 'Standard', 651: 'Standard', 652: 'Extra', 653: 'Standard', 654: 'Standard', 655: 'Standard', 656: 'Standard', 657: 'Standard', 658: 'Standard', 659: 'Standard', 660: 'Standard', 661: 'Standard', 662: 'Standard', 663: 'Standard', 664: 'Standard', 665: 'Standard', 666: 'Standard', 667: 'Extra', 668: 'Standard', 669: 'Standard', 670: 'Standard', 671: 'Standard', 672: 'Extra', 673: 'Extra', 674: 'Standard', 675: 'Standard', 676: 'Standard', 677: 'Standard', 678: 'Standard', 679: 'Extra', 680: 'Standard', 681: 'Extra', 682: 'Standard', 683: 'Standard', 684: 'Standard', 685: 'Standard', 686: 'Standard', 687: 'Extra', 688: 'Standard', 689: 'Standard', 690: 'Standard', 691: 'Standard', 692: 'Standard', 693: 'Standard', 694: 'Standard', 695: 'Standard', 696: 'Standard', 697: 'Standard', 698: 'Standard', 699: 'Extra', 700: 'Standard', 701: 'Standard', 702: 'Standard', 703: 'Standard', 704: 'Standard', 705: 'Standard', 706: 'Standard', 707: 'Standard', 708: 'Standard', 709: 'Standard', 710: 'Standard', 711: 'Standard', 712: 'Extra', 713: 'Standard', 714: 'Standard', 715: 'Standard', 716: 'Standard', 717: 'Standard', 718: 'Standard', 719: 'Standard', 720: 'Standard', 721: 'Standard', 722: 'Extra', 723: 'Standard', 724: 'Standard', 725: 'Standard', 726: 'Standard', 727: 'Standard', 728: 'Standard', 729: 'Extra', 730: 'Standard', 731: 'Standard', 732: 'Standard', 733: 'Standard', 734: 'Standard', 735: 'Standard', 736: 'Standard', 737: 'Standard', 738: 'Standard', 739: 'Standard', 740: 'Standard', 741: 'Standard', 742: 'Standard', 743: 'Standard', 744: 'Extra', 745: 'Standard', 746: 'Standard', 747: 'Standard', 748: 'Standard', 749: 'Standard', 750: 'Standard', 751: 'Standard', 752: 'Standard', 753: 'Standard', 754: 'Standard', 755: 'Standard', 756: 'Extra', 757: 'Standard', 758: 'Standard', 759: 'Standard', 760: 'Extra', 761: 'Standard', 762: 'Standard', 763: 'Standard', 764: 'Standard', 765: 'Standard', 766: 'Standard', 767: 'Standard', 768: 'Standard', 769: 'Standard', 770: 'Standard', 771: 'Standard', 772: 'Extra', 773: 'Extra', 774: 'Standard', 775: 'Standard', 776: 'Standard', 777: 'Extra', 778: 'Standard'}
}
I'm trying to visualize as simple as possible the overall advantage of Extra vs Standard (I.E: 'What is the edge of Extra over Standard?)
df[['views_per_day', 'status']].groupby('status').quantile(0.85).plot.bar()
What about using a boxplot? (eventually with a log scale):
import seaborn as sns
ax = sns.boxplot(data=df, x='status', y='views_per_day')
ax.set_yscale("log")
output:
Or a violinplot:
ax = sns.violinplot(data=df, x='status', y='views_per_day')
Related
Calculation percentage and plot using pivot-table
I need to plot a percentage graphic with a pivot table in pandas do you have any ideas? how I can calculate the percentage? thanks for your orientation!
This the code that I have :
df.pivot_table(index='obito', values=['asma', 'cardiopatia','diabetes','doenca_renal','obesidade']).T.plot(kind ='bar' , stacked = True)
This part of the dataframe that I have:
{'nome_munic': {66: 'Ferraz de Vasconcelos',
97: 'São Paulo',
100: 'São José dos Campos',
207: 'Mauá',
249: 'Cajamar',
258: 'Votuporanga',
285: 'Ferraz de Vasconcelos',
290: 'São Paulo',
345: 'São Pedro',
378: 'São Paulo'},
'codigo_ibge': {66: 3515707,
97: 3550308,
100: 3549904,
207: 3529401,
249: 3509205,
258: 3557105,
285: 3515707,
290: 3550308,
345: 3550407,
378: 3550308},
'idade': {66: 86,
97: 62,
100: 58,
207: 54,
249: 62,
258: 37,
285: 54,
290: 71,
345: 79,
378: 61},
'sexo': {66: 0,
97: 0,
100: 0,
207: 1,
249: 0,
258: 1,
285: 0,
290: 0,
345: 0,
378: 0},
'obito': {66: 1,
97: 0,
100: 0,
207: 1,
249: 1,
258: 1,
285: 0,
290: 1,
345: 1,
378: 0},
'asma': {66: 0,
97: 0,
100: 0,
207: 1,
249: 0,
258: 0,
285: 0,
290: 0,
345: 0,
378: 0},
'cardiopatia': {66: 1,
97: 0,
100: 1,
207: 1,
249: 1,
258: 0,
285: 1,
290: 1,
345: 0,
378: 0},
'diabetes': {66: 1,
97: 1,
100: 0,
207: 0,
249: 1,
258: 1,
285: 0,
290: 0,
345: 1,
378: 0},
'doenca_hematologica': {66: 0,
97: 0,
100: 0,
207: 0,
249: 0,
258: 0,
285: 0,
290: 0,
345: 0,
378: 0},
'doenca_hepatica': {66: 0,
97: 0,
100: 0,
207: 0,
249: 0,
258: 0,
285: 0,
290: 0,
345: 0,
378: 0},
'doenca_neurologica': {66: 0,
97: 0,
100: 0,
207: 0,
249: 0,
258: 0,
285: 0,
290: 1,
345: 0,
378: 0},
'doenca_renal': {66: 0,
97: 0,
100: 0,
207: 0,
249: 0,
258: 0,
285: 0,
290: 0,
345: 0,
378: 0},
'imunodepressao': {66: 0,
97: 0,
100: 1,
207: 0,
249: 0,
258: 0,
285: 0,
290: 0,
345: 0,
378: 0},
'obesidade': {66: 0,
97: 0,
100: 0,
207: 0,
249: 1,
258: 1,
285: 0,
290: 0,
345: 0,
378: 0},
'outros_fatores_de_risco': {66: 0,
97: 0,
100: 1,
207: 0,
249: 0,
258: 0,
285: 0,
290: 0,
345: 0,
378: 1},
'pneumopatia': {66: 0,
97: 1,
100: 0,
207: 0,
249: 0,
258: 0,
285: 0,
290: 0,
345: 0,
378: 0},
'puerpera': {66: 0,
97: 0,
100: 0,
207: 0,
249: 0,
258: 0,
285: 0,
290: 0,
345: 0,
378: 0},
'sindrome_de_down': {66: 0,
97: 0,
100: 0,
207: 0,
249: 0,
258: 0,
285: 0,
290: 0,
345: 0,
378: 0}}
Thanks for your support.
The default aggfunc is np.mean however it doesn’t compute the average per column or something like that, it computes the average of cells. In short it’s for every case the number of 1 divided by the number of 1 + the number of 0. This makes sense sometimes, but not for your data. >>> df[['asma', 'cardiopatia','diabetes','doenca_renal','obesidade', 'obito']] asma cardiopatia diabetes doenca_renal obesidade obito 66 0 1 1 0 0 1 97 0 0 1 0 0 0 100 0 1 0 0 0 0 207 1 1 0 0 0 1 249 0 1 1 0 1 1 258 0 0 1 0 1 1 285 0 1 0 0 0 0 290 0 1 0 0 0 1 345 0 0 1 0 0 1 378 0 0 0 0 0 0 >>> df.pivot_table(index='obito', values=['asma', 'cardiopatia','diabetes','doenca_renal','obesidade']) asma cardiopatia diabetes doenca_renal obesidade obito 0 0.000000 0.500000 0.250000 0 0.000000 1 0.166667 0.666667 0.666667 0 0.333333 Instead you probably want to count the total of patients per cell, then divide by column totals: >>> counts = df.pivot_table(index='obito', values=['asma', 'cardiopatia','diabetes','doenca_renal','obesidade'], aggfunc=np.sum) >>> counts / counts.sum() asma cardiopatia diabetes doenca_renal obesidade obito 0 0.0 0.333333 0.2 NaN 0.0 1 1.0 0.666667 0.8 NaN 1.0 Note how every column now sums to 1. doenca_renal is NaN because it has no patients at all in the sample, so the percentage is not defined. If you only want to plot the percentages that have obito=1, then you can do this: >>> from matplotlib import ticker >>> ax = (counts / counts.sum()).loc[1].plot.bar(rot=0) >>> ax.yaxis.set_major_formatter(ticker.PercentFormatter(xmax=1)) If you want to annotate the percentages on the bar, see this other question
Matching Buy Sell entries from two dataframes and creating a new one. Python 3.8 / W10
Python / Pandas.
Matching Buy and Sell entries row by row.
BuyDF and SellDF are obtained from one excel file and sorted as per ascending Time (column L).
The image shows how the matching has to be done.
Match Buy and Sell entries by Name following first in first out method.
Take very first entry (Name AAA) from BuyDF and match with very first / Top most entry (Name AAA) from SellDF and move the matching row from SellDF in front of corrosponding row of BuyDF and delete the row Sell DF.
Go back to BuyDF second entry and match SellDF entry and move the matching row from SellDF and move the matching row from SellDF in front of corrosponding row of BuyDF and the row is deleted from Sell DF ...... and so on.
For names which do not match leave the matching rows Blank.
The ascending order (Time / Column L) should not be changed to maintain first in first out.
Tried using merge but didn't work for me.
How to proceed ?
BuyDF
{'Date': {0: '2019-04-01', 1: '2019-04-01', 2: '2019-04-01', 3: '2019-04-01', 4: '2019-04-02', 5: '2019-04-02', 6: '2019-04-02', 7: '2019-04-02', 8: '2019-04-05'}, 'Name': {0: 'AAA', 1: 'AAA', 2: 'AAA', 3: 'AAA', 4: 'BBB', 5: 'CCC', 6: 'CCC', 7: 'BBB', 8: 'AAA'}, 'Ref': {0: 1, 1: 1, 2: 1, 3: 1, 4: 5, 5: 7, 6: 7, 7: 6, 8: 1}, 'Seg': {0: 'S', 1: 'S', 2: 'S', 3: 'S', 4: 'L', 5: 'XL', 6: 'XL', 7: 'L', 8: 'S'}, 'Trans': {0: 'buy', 1: 'buy', 2: 'buy', 3: 'buy', 4: 'buy', 5: 'buy', 6: 'buy', 7: 'buy', 8: 'buy'}, 'Qty': {0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1}, 'Price': {0: 225, 1: 225, 2: 225, 3: 225, 4: 210, 5: 210, 6: 210, 7: 210, 8: 225}, 'Order ID': {0: 8249, 1: 111, 2: 654, 3: 111, 4: 888, 5: 444, 6: 444, 7: 888, 8: 111}, 'Trade ID': {0: 1010, 1: 1010, 2: 1010, 3: 1010, 4: 4645, 5: 132, 6: 132, 7: 4700, 8: 1010}, 'Time': {0: '2019-04-01 11:05:18', 1: '2019-04-01 13:05:18', 2: '2019-04-01 13:05:18', 3: '2019-04-01 13:05:59', 4: '2019-04-02 13:20:05', 5: '2019-04-02 13:35:02', 6: '2019-04-02 13:35:02', 7: '2019-04-02 14:20:12', 8: '2019-04-05 13:05:18'}}
SellDF
{'Date': {5: '2019-04-01', 6: '2019-04-02', 7: '2019-04-02', 8: '2019-04-02', 13: '2019-04-03', 14: '2019-04-05', 15: '2019-04-05'}, 'Name': {5: 'AAA', 6: 'BBB', 7: 'BBB', 8: 'BBB', 13: 'DDD', 14: 'AAA', 15: 'AAA'}, 'Ref': {5: 3, 6: 2, 7: 2, 8: 2, 13: 8, 14: 4, 15: 4}, 'Seg': {5: 'L', 6: 'X', 7: 'X', 8: 'X', 13: 'XS', 14: 'L', 15: 'L'}, 'Trans': {5: 'sell', 6: 'sell', 7: 'sell', 8: 'sell', 13: 'sell', 14: 'sell', 15: 'sell'}, 'Qty': {5: 1, 6: 1, 7: 1, 8: 1, 13: 1, 14: 1, 15: 1}, 'Price': {5: 210, 6: 210, 7: 210, 8: 210, 13: 210, 14: 210, 15: 210}, 'Order ID': {5: 555, 6: 222, 7: 222, 8: 222, 13: 999, 14: 555, 15: 555}, 'Trade ID': {5: 1640, 6: 1532, 7: 1532, 8: 1532, 13: 14623, 14: 1645, 15: 1645}, 'Time': {5: '2019-04-01 14:13:40', 6: '2019-04-02 13:10:32', 7: '2019-04-02 13:10:32', 8: '2019-04-02 13:10:32', 13: '2019-04-03 15:25:50', 14: '2019-04-05 14:41:45', 15: '2019-04-05 14:41:45'}}
Image posted for ease of understanding.
summing up values in dictionary
I am working with networkX which is a graph based library in python. I have truncated data here because it is just a list of dictionaries.
I want to sum up the number of triangles in a graph.
import networkx as nx
for i in graphT:
print(nx.triangles(i))
This generates
{0: 1, 1: 0, 2: 0, 3: 1, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 1, 11: 0, 12: 2, 13: 0, 14: 2, 15: 1, 16: 1, 17: 0, 18: 0, 19: 1, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 5, 26: 1, 27: 0, 28: 0, 29: 1, 30: 0, 31: 0, 32: 0, 33: 4, 34: 1, 35: 3, 36: 1, 37: 1, 38: 2, 39: 1}
{0: 1, 1: 0, 2: 0, 3: 1, 4: 2, 5: 0, 6: 0, 7: 0, 8: 0, 9: 2, 10: 2, 11: 0, 12: 2, 13: 2, 14: 3, 15: 2, 16: 1, 17: 0, 18: 1, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 2, 26: 2, 27: 0, 28: 0, 29: 1, 30: 0, 31: 0, 32: 0, 33: 2, 34: 0, 35: 2, 36: 1, 37: 1, 38: 1, 39: 2}
{0: 1, 1: 1, 2: 0, 3: 2, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 2, 11: 0, 12: 4, 13: 0, 14: 3, 15: 4, 16: 1, 17: 1, 18: 0, 19: 0, 20: 0, 21: 0, 22: 1, 23: 0, 24: 0, 25: 3, 26: 2, 27: 0, 28: 0, 29: 1, 30: 0, 31: 0, 32: 0, 33: 1, 34: 1, 35: 2, 36: 1, 37: 1, 38: 2, 39: 2}
......
Now with that, I want to sum the values
for which
for i in graphT:
#print(nx.triangles(i))
for k, v in nx.triangles(i).items():
#print(i.number_of_edges(), v)
#print(jt.append(v))
vi= v
print(k, vi)
break
which generates tuple of for key '0'. I have truncated data here with break statement.
(0, 1)
(0, 1)
(0, 1)
(0, 1)
(0, 1)
(0, 1)
(0, 1)
(0, 5)
(0, 2)
(0, 4)
(0, 3)
(0, 5)
(0, 2)
(0, 3)
(0, 4)
(0, 1)
(0, 4)
(0, 1)
(0, 8)
(0, 5)
(0, 2)
(0, 4)
(0, 3)
(0, 5)
(0, 6)
(0, 3)
(0, 3)
(0, 11)
(0, 7)
(0, 18)
(0, 9)
(0, 8)
(0, 4)
(0, 12)
(0, 22)
(0, 14)
(0, 23)
(0, 18)
(0, 2)
(0, 41)
(0, 8)
(0, 29)
(0, 13)
(0, 12)
(0, 53)
(0, 12)
How would I sum up values on this this tuple which is second element? If I use sum(v) or sum(vi) I get TypeError: 'int' object is not iterable.
This gives me solution:
j= [(sum(n for _, n in nx.triangles(i).items()), i.number_of_edges()) for i in graphT]
You can access all of the values of a dictionary with values. Then sum those. So for this case: import networkx as nx G = nx.fast_gnp_random_graph(1000,0.05) triangle_counts = nx.triangles(G) triangle_sum = sum(triangle_counts.values()) number_of_triangles = triangle_sum/3 print(number_of_triangles) > 20444.0 Note that each triangle is counted once per node, so I've divided by 3.
C stack usage error with SQL in R notebook
Problem
I just started using R notebooks and was impressed with being able to use SQL chunks. However I have run into a bizarre problem.
My code within the notebook looks something like the following
```{r}
library(RSQLite)
library(plyr)
library(Matrix)
```
```{r}
sessionInfo()
```
```{r}
dbName = '/home/my_db.db'
db <- dbConnect(SQLite(), dbname=dbName)
```
```{sql connection=db, max.print=10}
SELECT * FROM EVENTS_ID
```
The error I get is
Error: C stack usage 7969396 is too close to the limit
Failed to execute SQL chunk
Now I have a further SQL query further in the notebook that when executed gives a similar error with a different number,
Error: C stack usage 7969604 is too close to the limit
Failed to execute SQL chunk
The most strange thing about this however is that the code was running fine previously and I've made no changes that I can think of.
Attempted Solutions/Debugging
1) Restarted computer. Still get the same errors.
2) Changed the code to the following to rule out database table error
```{sql connection=db}
SELECT 1
```
Still got the same error.
Current Workaround
I can workaround the issue by doing everything in R instead of the SQL chunk i.e.
```{r}
query <- gsub(pattern='\\s',replacement=" ",x="SELECT * FROM EVENTS_ID LIMIT 10")
dbGetQuery(db, query)
```
This works but I would like to use the SQL chunks if possible.
Further Info
When running traceback() after the error I get the following which may suggest there is some kind of as.vector recursion error
936: as.vector(x, mode)
935: as.vector(x, mode)
934: as.vector(x, mode)
933: as.vector(x, mode)
932: as.vector(x, mode)
931: as.vector(x, mode)
930: as.vector(x, mode)
929: as.vector(x, mode)
928: as.vector(x, mode)
927: as.vector(x, mode)
926: as.vector(x, mode)
925: as.vector(x, mode)
924: as.vector(x, mode)
923: as.vector(x, mode)
922: as.vector(x, mode)
921: as.vector(x, mode)
920: as.vector(x, mode)
919: as.vector(x, mode)
918: as.vector(x, mode)
917: as.vector(x, mode)
916: as.vector(x, mode)
915: as.vector(x, mode)
914: as.vector(x, mode)
913: as.vector(x, mode)
912: as.vector(x, mode)
911: as.vector(x, mode)
910: as.vector(x, mode)
909: as.vector(x, mode)
908: as.vector(x, mode)
907: as.vector(x, mode)
906: as.vector(x, mode)
905: as.vector(x, mode)
904: as.vector(x, mode)
903: as.vector(x, mode)
902: as.vector(x, mode)
901: as.vector(x, mode)
900: as.vector(x, mode)
899: as.vector(x, mode)
898: as.vector(x, mode)
897: as.vector(x, mode)
896: as.vector(x, mode)
895: as.vector(x, mode)
894: as.vector(x, mode)
893: as.vector(x, mode)
892: as.vector(x, mode)
891: as.vector(x, mode)
890: as.vector(x, mode)
889: as.vector(x, mode)
888: as.vector(x, mode)
887: as.vector(x, mode)
886: as.vector(x, mode)
885: as.vector(x, mode)
884: as.vector(x, mode)
883: as.vector(x, mode)
882: as.vector(x, mode)
881: as.vector(x, mode)
880: as.vector(x, mode)
879: as.vector(x, mode)
878: as.vector(x, mode)
877: as.vector(x, mode)
876: as.vector(x, mode)
875: as.vector(x, mode)
874: as.vector(x, mode)
873: as.vector(x, mode)
872: as.vector(x, mode)
871: as.vector(x, mode)
870: as.vector(x, mode)
869: as.vector(x, mode)
868: as.vector(x, mode)
867: as.vector(x, mode)
866: as.vector(x, mode)
865: as.vector(x, mode)
864: as.vector(x, mode)
863: as.vector(x, mode)
862: as.vector(x, mode)
861: as.vector(x, mode)
860: as.vector(x, mode)
859: as.vector(x, mode)
858: as.vector(x, mode)
857: as.vector(x, mode)
856: as.vector(x, mode)
855: as.vector(x, mode)
854: as.vector(x, mode)
853: as.vector(x, mode)
852: as.vector(x, mode)
851: as.vector(x, mode)
850: as.vector(x, mode)
849: as.vector(x, mode)
848: as.vector(x, mode)
847: as.vector(x, mode)
846: as.vector(x, mode)
845: as.vector(x, mode)
844: as.vector(x, mode)
843: as.vector(x, mode)
842: as.vector(x, mode)
841: as.vector(x, mode)
840: as.vector(x, mode)
839: as.vector(x, mode)
838: as.vector(x, mode)
837: as.vector(x, mode)
836: as.vector(x, mode)
835: as.vector(x, mode)
834: as.vector(x, mode)
833: as.vector(x, mode)
832: as.vector(x, mode)
831: as.vector(x, mode)
830: as.vector(x, mode)
829: as.vector(x, mode)
828: as.vector(x, mode)
827: as.vector(x, mode)
826: as.vector(x, mode)
825: as.vector(x, mode)
824: as.vector(x, mode)
823: as.vector(x, mode)
822: as.vector(x, mode)
821: as.vector(x, mode)
820: as.vector(x, mode)
819: as.vector(x, mode)
818: as.vector(x, mode)
817: as.vector(x, mode)
816: as.vector(x, mode)
815: as.vector(x, mode)
814: as.vector(x, mode)
813: as.vector(x, mode)
812: as.vector(x, mode)
811: as.vector(x, mode)
810: as.vector(x, mode)
809: as.vector(x, mode)
808: as.vector(x, mode)
807: as.vector(x, mode)
806: as.vector(x, mode)
805: as.vector(x, mode)
804: as.vector(x, mode)
803: as.vector(x, mode)
802: as.vector(x, mode)
801: as.vector(x, mode)
800: as.vector(x, mode)
799: as.vector(x, mode)
798: as.vector(x, mode)
797: as.vector(x, mode)
796: as.vector(x, mode)
795: as.vector(x, mode)
794: as.vector(x, mode)
793: as.vector(x, mode)
792: as.vector(x, mode)
791: as.vector(x, mode)
790: as.vector(x, mode)
789: as.vector(x, mode)
788: as.vector(x, mode)
787: as.vector(x, mode)
786: as.vector(x, mode)
785: as.vector(x, mode)
784: as.vector(x, mode)
783: as.vector(x, mode)
782: as.vector(x, mode)
781: as.vector(x, mode)
780: as.vector(x, mode)
779: as.vector(x, mode)
778: as.vector(x, mode)
777: as.vector(x, mode)
776: as.vector(x, mode)
775: as.vector(x, mode)
774: as.vector(x, mode)
773: as.vector(x, mode)
772: as.vector(x, mode)
771: as.vector(x, mode)
770: as.vector(x, mode)
769: as.vector(x, mode)
768: as.vector(x, mode)
767: as.vector(x, mode)
766: as.vector(x, mode)
765: as.vector(x, mode)
764: as.vector(x, mode)
763: as.vector(x, mode)
762: as.vector(x, mode)
761: as.vector(x, mode)
760: as.vector(x, mode)
759: as.vector(x, mode)
758: as.vector(x, mode)
757: as.vector(x, mode)
756: as.vector(x, mode)
755: as.vector(x, mode)
754: as.vector(x, mode)
753: as.vector(x, mode)
752: as.vector(x, mode)
751: as.vector(x, mode)
750: as.vector(x, mode)
749: as.vector(x, mode)
748: as.vector(x, mode)
747: as.vector(x, mode)
746: as.vector(x, mode)
745: as.vector(x, mode)
744: as.vector(x, mode)
743: as.vector(x, mode)
742: as.vector(x, mode)
741: as.vector(x, mode)
740: as.vector(x, mode)
739: as.vector(x, mode)
738: as.vector(x, mode)
737: as.vector(x, mode)
736: as.vector(x, mode)
735: as.vector(x, mode)
734: as.vector(x, mode)
733: as.vector(x, mode)
732: as.vector(x, mode)
731: as.vector(x, mode)
730: as.vector(x, mode)
729: as.vector(x, mode)
728: as.vector(x, mode)
727: as.vector(x, mode)
726: as.vector(x, mode)
725: as.vector(x, mode)
724: as.vector(x, mode)
723: as.vector(x, mode)
722: as.vector(x, mode)
721: as.vector(x, mode)
720: as.vector(x, mode)
719: as.vector(x, mode)
718: as.vector(x, mode)
717: as.vector(x, mode)
716: as.vector(x, mode)
715: as.vector(x, mode)
714: as.vector(x, mode)
713: as.vector(x, mode)
712: as.vector(x, mode)
711: as.vector(x, mode)
710: as.vector(x, mode)
709: as.vector(x, mode)
708: as.vector(x, mode)
707: as.vector(x, mode)
706: as.vector(x, mode)
705: as.vector(x, mode)
704: as.vector(x, mode)
703: as.vector(x, mode)
702: as.vector(x, mode)
701: as.vector(x, mode)
700: as.vector(x, mode)
699: as.vector(x, mode)
698: as.vector(x, mode)
697: as.vector(x, mode)
696: as.vector(x, mode)
695: as.vector(x, mode)
694: as.vector(x, mode)
693: as.vector(x, mode)
692: as.vector(x, mode)
691: as.vector(x, mode)
690: as.vector(x, mode)
689: as.vector(x, mode)
688: as.vector(x, mode)
687: as.vector(x, mode)
686: as.vector(x, mode)
685: as.vector(x, mode)
684: as.vector(x, mode)
683: as.vector(x, mode)
682: as.vector(x, mode)
681: as.vector(x, mode)
680: as.vector(x, mode)
679: as.vector(x, mode)
678: as.vector(x, mode)
677: as.vector(x, mode)
676: as.vector(x, mode)
675: as.vector(x, mode)
674: as.vector(x, mode)
673: as.vector(x, mode)
672: as.vector(x, mode)
671: as.vector(x, mode)
670: as.vector(x, mode)
669: as.vector(x, mode)
668: as.vector(x, mode)
667: as.vector(x, mode)
666: as.vector(x, mode)
665: as.vector(x, mode)
664: as.vector(x, mode)
663: as.vector(x, mode)
662: as.vector(x, mode)
661: as.vector(x, mode)
660: as.vector(x, mode)
659: as.vector(x, mode)
658: as.vector(x, mode)
657: as.vector(x, mode)
656: as.vector(x, mode)
655: as.vector(x, mode)
654: as.vector(x, mode)
653: as.vector(x, mode)
652: as.vector(x, mode)
651: as.vector(x, mode)
650: as.vector(x, mode)
649: as.vector(x, mode)
648: as.vector(x, mode)
647: as.vector(x, mode)
646: as.vector(x, mode)
645: as.vector(x, mode)
644: as.vector(x, mode)
643: as.vector(x, mode)
642: as.vector(x, mode)
641: as.vector(x, mode)
640: as.vector(x, mode)
639: as.vector(x, mode)
638: as.vector(x, mode)
637: as.vector(x, mode)
636: as.vector(x, mode)
635: as.vector(x, mode)
634: as.vector(x, mode)
633: as.vector(x, mode)
632: as.vector(x, mode)
631: as.vector(x, mode)
630: as.vector(x, mode)
629: as.vector(x, mode)
628: as.vector(x, mode)
627: as.vector(x, mode)
626: as.vector(x, mode)
625: as.vector(x, mode)
624: as.vector(x, mode)
623: as.vector(x, mode)
622: as.vector(x, mode)
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65: as.vector(x, mode)
64: as.vector(x, mode)
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62: as.vector(x, mode)
61: as.vector(x, mode)
60: as.vector(x, mode)
59: as.vector(x, mode)
58: as.vector(x, mode)
57: as.vector(x, mode)
56: as.vector(x, mode)
55: as.vector(x, mode)
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53: as.vector(x, mode)
52: as.vector(x, mode)
51: as.vector(x, mode)
50: as.vector(x, mode)
49: as.vector(x, mode)
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47: as.vector(x, mode)
46: as.vector(x, mode)
45: as.vector(x, mode)
44: as.vector(x, mode)
43: as.vector(x, mode)
42: as.vector(x, mode)
41: as.vector(x, mode)
40: as.vector(x, mode)
39: as.vector(x, mode)
38: as.vector(x, mode)
37: as.vector(x, mode)
36: as.vector(x, mode)
35: as.vector(x, mode)
34: as.vector(x, mode)
33: as.vector(x, mode)
32: as.vector(x, mode)
31: as.vector(x, mode)
30: as.vector(x, mode)
29: as.vector(x, mode)
28: as.vector(x, mode)
27: as.vector(x, mode)
26: as.vector(x, mode)
25: as.vector(x, mode)
24: as.vector(x, mode)
23: as.vector(x, mode)
22: as.vector(x, mode)
21: as.vector(x, mode)
20: as.vector(x, mode)
19: as.vector(x, mode)
18: as.vector(x, mode)
17: as.vector(x, mode)
16: as.vector(x, mode)
15: as.vector(x, mode)
14: as.vector(x, mode)
13: as.vector(x, mode)
12: as.vector(x, mode)
11: as.vector(x, mode)
10: as.vector(x, mode)
9: as.vector(x, "character")
8: as.vector(x, "character")
7: as.character.default(statement)
6: as.character(statement)
5: sqliteSendQuery(conn, statement)
4: .local(conn, statement, ...)
3: DBI::dbSendQuery(conn, query)
2: DBI::dbSendQuery(conn, query)
1: (function (sql, outputFile, options)
{
is_sql_update_query <- function(query) {
query <- gsub("^\\s*--.*\n", "", query)
if (grepl("^\\s*\\/\\*.*", query)) {
query <- gsub(".*\\*\\/", "", query)
}
grepl("^\\s*(INSERT|UPDATE|DELETE|CREATE).*", query,
ignore.case = TRUE)
}
dir.create(dirname(outputFile), recursive = TRUE, showWarnings = FALSE)
max.print <- if (is.null(options$max.print))
getOption("max.print", 1000)
else as.numeric(options$max.print)
max.print <- if (is.null(options$sql.max.print))
max.print
else as.numeric(options$sql.max.print)
if (is.null(options$connection))
stop("The 'connection' option (DBI connection) is required for sql chunks.")
conn <- get(options$connection, envir = globalenv())
if (is.null(conn))
stop("The 'connection' option must be a valid DBI connection.")
varnames_from_sql <- function(conn, sql) {
varPos <- DBI::sqlParseVariables(conn, sql)
if (length(varPos$start) > 0) {
varNames <- substring(sql, varPos$start, varPos$end)
sub("^\\?", "", varNames)
}
}
mexists <- function(x, env = globalenv(), inherits = TRUE) {
vapply(x, exists, logical(1), where = env, inherits = inherits)
}
interpolate_from_env <- function(conn, sql, env = globalenv(),
inherits = TRUE) {
names <- unique(varnames_from_sql(conn, sql))
names_missing <- names[!mexists(names, env, inherits)]
if (length(names_missing) > 0) {
stop("Object(s) not found: ", paste("\"", names_missing,
"\"", collapse = ", "))
}
args <- if (length(names) > 0)
setNames(mget(names, envir = env, inherits = inherits),
names)
do.call(DBI::sqlInterpolate, c(list(conn, sql), args))
}
varname <- options$output.var
query <- interpolate_from_env(conn, sql)
if (is.null(varname) && max.print > 0 && !is_sql_update_query(query)) {
res <- DBI::dbSendQuery(conn, query)
data <- DBI::dbFetch(res, n = max.print)
DBI::dbClearResult(res)
}
else {
data <- DBI::dbGetQuery(conn, query)
}
if (!is.null(varname)) {
assign(varname, data, envir = globalenv())
}
else {
x <- data
save(x, file = outputFile)
}
})("SELECT * FROM EVENTS_ID", "/home/.rstudio-desktop/notebooks/567F01E3-Notebook/1/61A0FD15/csyikkfh4nozt/00006b.rdf",
list(connection = "db", engine = "sql", label = "unnamed-chunk-7",
max.print = 10))
If I run Cstack_info() after the error I get the following
size current direction eval_depth
7969177 15104 1 2
Pandas Multivariate Linear Regression by Group and Saving Results as csv
I am trying to calculate linear regression of Y=C-A column, x = ['Plate X', 'Plate Y', 'Field X'] and group those values by Drum and Plate. Additional question - how to save results as a file, csv preferable.
Is pandas package is sufficient for this task or other package needed.
Thank you
There is my data set:
DF = {'A': {0: 305.03277000000003,
1: 304.42513500000001,
2: 305.119575,
3: 304.42513500000001,
4: 304.07791500000002,
5: 304.85916000000003,
6: 305.72721000000001,
7: 305.81401499999998,
8: 304.07791500000002,
9: 305.03277000000003,
10: 304.68554999999998,
11: 304.945965,
12: 303.38347499999998,
13: 304.945965,
14: 304.51193999999998,
15: 304.25152500000002,
16: 304.51193999999998,
17: 304.25152500000002,
18: 304.42513500000001,
19: 304.85916000000003,
20: 303.8175,
21: 305.119575,
22: 304.59874500000001,
23: 304.68554999999998,
24: 304.33832999999998,
25: 303.90430499999997,
26: 304.68554999999998,
27: 304.772355,
28: 304.59874500000001,
29: 304.772355,
30: 304.59874500000001,
31: 305.119575,
32: 305.37998999999996,
33: 304.59874500000001,
34: 304.42513500000001,
35: 304.33832999999998,
36: 304.51193999999998,
37: 305.46679499999999,
38: 304.59874500000001,
39: 305.29318499999999,
40: 304.85916000000003,
41: 305.29318499999999,
42: 305.119575,
43: 304.945965,
44: 305.29318499999999,
45: 304.85916000000003,
46: 305.72721000000001,
47: 306.16123500000003,
48: 305.37998999999996,
49: 305.03277000000003,
50: 305.20637999999997,
51: 304.51193999999998,
52: 308.33136000000002,
53: 305.81401499999998,
54: 305.55360000000002,
55: 306.42165,
56: 305.64040499999999,
57: 305.29318499999999,
58: 305.37998999999996,
59: 304.772355,
60: 305.37998999999996,
61: 305.72721000000001,
62: 305.90082000000001,
63: 305.64040499999999,
64: 305.81401499999998,
65: 304.85916000000003,
66: 305.20637999999997,
67: 306.42165,
68: 305.64040499999999,
69: 305.55360000000002,
70: 304.59874500000001,
71: 305.55360000000002,
72: 306.07443000000001,
73: 306.42165,
74: 305.98762499999998,
75: 306.68206499999997,
76: 305.03277000000003,
77: 305.46679499999999,
78: 306.42165,
79: 304.85916000000003,
80: 304.51193999999998,
81: 303.8175,
82: 304.51193999999998,
83: 304.16472000000005,
84: 304.51193999999998,
85: 303.73069500000003,
86: 303.29667000000001,
87: 304.68554999999998,
88: 303.73069500000003,
89: 304.42513500000001,
90: 304.51193999999998,
91: 304.16472000000005,
92: 304.945965,
93: 304.772355,
94: 304.42513500000001,
95: 304.16472000000005,
96: 305.119575,
97: 304.16472000000005,
98: 304.25152500000002,
99: 305.20637999999997},
'B': {0: 311.10912000000002,
1: 310.93551000000002,
2: 313.279245,
3: 313.19243999999998,
4: 309.11260499999997,
5: 309.0258,
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49: 310.501485,
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55: 311.54314500000004,
56: 313.279245,
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60: 312.15078,
61: 312.15078,
62: 313.452855,
63: 311.02231499999999,
64: 311.02231499999999,
65: 311.28272999999996,
66: 311.02231499999999,
67: 307.897335,
68: 313.19243999999998,
69: 311.97717,
70: 311.10912000000002,
71: 312.58480499999996,
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73: 315.01534500000002,
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76: 311.80356,
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99: 310.32787500000001},
'C': {0: 305.72721000000001,
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'C-A': {0: 0.69443999999999995,
1: 1.5798510000000001,
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3: 2.1701250000000001,
4: 1.4062410000000001,
5: 0.381942,
6: 0.55555200000000005,
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8: 2.7256770000000001,
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10: 1.423602,
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31: 1.232631,
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45: 1.631934,
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96: 0.32985900000000001,
97: 1.145826,
98: 1.059021,
99: 1.249992},
'Drum': {0: 'LAAA',
1: 'LAAA',
2: 'LAAA',
3: 'LAAA',
4: 'LAAA',
5: 'LAAA',
6: 'LAAA',
7: 'LAAA',
8: 'LAAA',
9: 'LAAA',
10: 'LAAA',
11: 'LAAA',
12: 'LAAA',
13: 'LAAA',
14: 'LAAA',
15: 'LAAA',
16: 'LAAA',
17: 'LAAA',
18: 'LAAA',
19: 'LAAA',
20: 'LAAA',
21: 'LAAA',
22: 'LAAA',
23: 'LAAA',
24: 'LAAA',
25: 'LAAA',
26: 'LAAA',
27: 'LAAA',
28: 'LAAA',
29: 'LAAA',
30: 'LAAA',
31: 'LAAA',
32: 'LAAA',
33: 'LAAA',
34: 'LAAA',
35: 'LAAA',
36: 'LAAA',
37: 'LAAA',
38: 'LAAA',
39: 'LAAA',
40: 'LAAA',
41: 'LAAA',
42: 'LAAA',
43: 'LAAA',
44: 'LAAA',
45: 'LAAA',
46: 'LAAA',
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48: 'LAAA',
49: 'LAAA',
50: 'LAAA',
51: 'LAAA',
52: 'LAAA',
53: 'LAAA',
54: 'LAAA',
55: 'LAAA',
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57: 'LAAA',
58: 'LAAA',
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60: 'LAAA',
61: 'LAAA',
62: 'LAAA',
63: 'LAAA',
64: 'LAAA',
65: 'LAAA',
66: 'LAAA',
67: 'LAAA',
68: 'LAAA',
69: 'LAAA',
70: 'LAAA',
71: 'LAAA',
72: 'LAAA',
73: 'LAAA',
74: 'LAAA',
75: 'LAAA',
76: 'LAAA',
77: 'LAAA',
78: 'LAAA',
79: 'LAAA',
80: 'LAAA',
81: 'LAAA',
82: 'LAAA',
83: 'LAAA',
84: 'LAAA',
85: 'LAAA',
86: 'LAAA',
87: 'LAAA',
88: 'LAAA',
89: 'LAAA',
90: 'LAAA',
91: 'LAAA',
92: 'LAAA',
93: 'LAAA',
94: 'LAAA',
95: 'LAAA',
96: 'LAAA',
97: 'LAAA',
98: 'LAAA',
99: 'LAAA'},
'FIELD X': {0: 4.7949800000000007,
1: -5.5198839999999993,
2: 4.7949800000000007,
3: 4.7949800000000007,
4: -5.5198839999999993,
5: 4.7949800000000007,
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96: 4.7949800000000007,
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'FIELD Y': {0: 1.8893500000000001,
1: 1.8893500000000001,
2: 1.8893500000000001,
3: 1.8893500000000001,
4: 1.8893500000000001,
5: 1.8893500000000001,
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'Plate': {0: 72,
1: 72,
2: 72,
3: 72,
4: 72,
5: 72,
6: 72,
7: 72,
8: 72,
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10: 72,
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40: 72,
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43: 72,
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45: 72,
46: 72,
47: 72,
48: 72,
49: 72,
50: 72,
51: 72,
52: 72,
53: 72,
54: 72,
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98: 131,
99: 131},
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17: -72.244876000000005,
18: -72.244876000000005,
19: -51.615147999999998,
20: -51.615147999999998,
21: -51.615147999999998,
22: -51.615147999999998,
23: -51.615147999999998,
24: -30.985420000000001,
25: -30.985420000000001,
26: -30.985420000000001,
27: -30.985420000000001,
28: -30.985420000000001,
29: -30.985420000000001,
30: -30.985420000000001,
31: -30.985420000000001,
32: -10.355691999999999,
33: -10.355691999999999,
34: -10.355691999999999,
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36: -10.355691999999999,
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43: 10.274036000000001,
44: 10.274036000000001,
45: 10.274036000000001,
46: 30.903764000000002,
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48: 30.903764000000002,
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50: 30.903764000000002,
51: 30.903764000000002,
52: 30.903764000000002,
53: 30.903764000000002,
54: 51.533491999999995,
55: 51.533491999999995,
56: 51.533491999999995,
57: 51.533491999999995,
58: 51.533491999999995,
59: 51.533491999999995,
60: 51.533491999999995,
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63: 72.163219999999995,
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65: 72.163219999999995,
66: 72.163219999999995,
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68: 92.792947999999996,
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70: 92.792947999999996,
71: 92.792947999999996,
72: 113.422676,
73: 113.422676,
74: 113.422676,
75: 113.422676,
76: 113.422676,
77: 134.052404,
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17: 81.1965,
18: 108.2487,
19: -81.116699999999994,
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21: 0.039899999999999998,
22: 54.144300000000001,
23: 81.1965,
24: -135.22110000000001,
25: -108.16889999999999,
26: -54.064500000000002,
27: -27.0123,
28: 27.092099999999999,
29: 54.144300000000001,
30: 108.2487,
31: 135.30090000000001,
32: -108.16889999999999,
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34: -27.0123,
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36: 27.092099999999999,
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98: 98,
99: 99}}
From your question it doesnt sound like you want a multivariate regression (i.e. multiple Y's). If you're just predicting a single Y from multiple X's, you can do it like this with pandas, then save the results to a txt file:
import pandas as pd
df = pd.DataFrame(DF)
res = pd.stats.api.ols(y=df['C-A'], x=df[['Plate X','Plate Y','FIELD X']])
file = open("C:/Users/Simon/Desktop/results.txt", "w")
file.write(str(res))
file.close()
You mentioned in the question that you want to group the analyses by Drum and Plate. However, every value is the same for the Drum rows. If you want to group by Plate, however, and then run OLS on each subgroup, you can do something like this:
import pandas as pd
df = pd.DataFrame(DF)
results = []
def ols_res(df):
results.append( pd.stats.api.ols(y=df['C-A'], x=df[['Plate X','Plate Y','FIELD X']]))
df.groupby('Plate').apply(lambda newdf: ols_res(newdf))
file = open("C:/Users/Simon/Desktop/results.txt", "w")
for el in results:
file.write(str(el))
file.close()
If you want to also group by Drum, and note which drum/plate combination each analysis is for, you can do something like this and just add some extra txt to the results file:
import pandas as pd
df = pd.DataFrame(DF)
results = []
def ols_res(df):
curCombo = "plate:" + str(df["Plate"].mean()) + ", drum:" + str(df["Drum"].unique())
regression_results = pd.stats.api.ols(y=df['C-A'], x=df[['Plate X','Plate Y','FIELD X']])
results.append([curCombo, regression_results])
df.groupby(['Plate', 'Drum']).apply(lambda newdf: ols_res(newdf))
file = open("C:/Users/Simon/Desktop/results.txt", "w")
for el in results:
file.write(str(el))
file.write("\n\n")
file.close()