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- gather_and_final_processing_finnish.ipynb +1501 -0
- process_initial_and_end_fi_fiiltering.ipynb +1727 -0
- process_zst_to_parquet_new.ipynb +3149 -0
- unzip_files.ipynb +312 -0
gather_and_final_processing_finnish.ipynb
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 4,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [],
|
| 8 |
+
"source": [
|
| 9 |
+
"import pandas as pd\n",
|
| 10 |
+
"import os \n",
|
| 11 |
+
"\n",
|
| 12 |
+
"folders = os.listdir(os.getcwd() + os.sep + 'finnish')\n",
|
| 13 |
+
"\n",
|
| 14 |
+
"paths_to_folders = [os.getcwd() + os.sep + 'finnish' + os.sep + folder for folder in folders]"
|
| 15 |
+
]
|
| 16 |
+
},
|
| 17 |
+
{
|
| 18 |
+
"cell_type": "code",
|
| 19 |
+
"execution_count": 9,
|
| 20 |
+
"metadata": {},
|
| 21 |
+
"outputs": [],
|
| 22 |
+
"source": [
|
| 23 |
+
"filepaths_all = []\n",
|
| 24 |
+
"for path in paths_to_folders:\n",
|
| 25 |
+
" filepaths = [path + os.sep + file for file in os.listdir(path)]\n",
|
| 26 |
+
" filepaths_all.extend(filepaths)"
|
| 27 |
+
]
|
| 28 |
+
},
|
| 29 |
+
{
|
| 30 |
+
"cell_type": "code",
|
| 31 |
+
"execution_count": 11,
|
| 32 |
+
"metadata": {},
|
| 33 |
+
"outputs": [],
|
| 34 |
+
"source": [
|
| 35 |
+
"asd = pd.read_parquet(filepaths_all[0])"
|
| 36 |
+
]
|
| 37 |
+
},
|
| 38 |
+
{
|
| 39 |
+
"cell_type": "code",
|
| 40 |
+
"execution_count": 26,
|
| 41 |
+
"metadata": {},
|
| 42 |
+
"outputs": [],
|
| 43 |
+
"source": [
|
| 44 |
+
"import datetime\n",
|
| 45 |
+
"def add_year_month_time_of_day(row):\n",
|
| 46 |
+
" row['created_utc'] = int(row['created_utc'])\n",
|
| 47 |
+
" dt_utc_native = datetime.datetime.utcfromtimestamp(row['created_utc'])\n",
|
| 48 |
+
" row['year'] = dt_utc_native.year\n",
|
| 49 |
+
" row['day'] = dt_utc_native.day\n",
|
| 50 |
+
" row['month'] = dt_utc_native.month\n",
|
| 51 |
+
" row['time'] = dt_utc_native.strftime(\"%H:%M:%S\")\n",
|
| 52 |
+
" return row"
|
| 53 |
+
]
|
| 54 |
+
},
|
| 55 |
+
{
|
| 56 |
+
"cell_type": "code",
|
| 57 |
+
"execution_count": 20,
|
| 58 |
+
"metadata": {},
|
| 59 |
+
"outputs": [],
|
| 60 |
+
"source": [
|
| 61 |
+
"df = asd.apply(lambda row: add_year_month_time_of_day(row), axis=1)"
|
| 62 |
+
]
|
| 63 |
+
},
|
| 64 |
+
{
|
| 65 |
+
"cell_type": "code",
|
| 66 |
+
"execution_count": 27,
|
| 67 |
+
"metadata": {},
|
| 68 |
+
"outputs": [
|
| 69 |
+
{
|
| 70 |
+
"name": "stdout",
|
| 71 |
+
"output_type": "stream",
|
| 72 |
+
"text": [
|
| 73 |
+
"1/1079\n",
|
| 74 |
+
"2/1079\n",
|
| 75 |
+
"3/1079\n",
|
| 76 |
+
"4/1079\n",
|
| 77 |
+
"5/1079\n",
|
| 78 |
+
"6/1079\n",
|
| 79 |
+
"7/1079\n",
|
| 80 |
+
"8/1079\n",
|
| 81 |
+
"9/1079\n",
|
| 82 |
+
"10/1079\n",
|
| 83 |
+
"11/1079\n",
|
| 84 |
+
"12/1079\n",
|
| 85 |
+
"13/1079\n",
|
| 86 |
+
"14/1079\n",
|
| 87 |
+
"15/1079\n",
|
| 88 |
+
"16/1079\n",
|
| 89 |
+
"17/1079\n",
|
| 90 |
+
"18/1079\n",
|
| 91 |
+
"19/1079\n",
|
| 92 |
+
"20/1079\n",
|
| 93 |
+
"21/1079\n",
|
| 94 |
+
"22/1079\n",
|
| 95 |
+
"23/1079\n",
|
| 96 |
+
"24/1079\n",
|
| 97 |
+
"25/1079\n",
|
| 98 |
+
"26/1079\n",
|
| 99 |
+
"27/1079\n",
|
| 100 |
+
"28/1079\n",
|
| 101 |
+
"29/1079\n",
|
| 102 |
+
"30/1079\n",
|
| 103 |
+
"31/1079\n",
|
| 104 |
+
"32/1079\n",
|
| 105 |
+
"33/1079\n",
|
| 106 |
+
"34/1079\n",
|
| 107 |
+
"35/1079\n",
|
| 108 |
+
"36/1079\n",
|
| 109 |
+
"37/1079\n",
|
| 110 |
+
"38/1079\n",
|
| 111 |
+
"39/1079\n",
|
| 112 |
+
"40/1079\n",
|
| 113 |
+
"41/1079\n",
|
| 114 |
+
"42/1079\n",
|
| 115 |
+
"43/1079\n",
|
| 116 |
+
"44/1079\n",
|
| 117 |
+
"45/1079\n",
|
| 118 |
+
"46/1079\n",
|
| 119 |
+
"47/1079\n",
|
| 120 |
+
"48/1079\n",
|
| 121 |
+
"49/1079\n",
|
| 122 |
+
"50/1079\n",
|
| 123 |
+
"51/1079\n",
|
| 124 |
+
"52/1079\n",
|
| 125 |
+
"53/1079\n",
|
| 126 |
+
"54/1079\n",
|
| 127 |
+
"55/1079\n",
|
| 128 |
+
"56/1079\n",
|
| 129 |
+
"57/1079\n",
|
| 130 |
+
"58/1079\n",
|
| 131 |
+
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|
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+
"260/1079\n",
|
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"261/1079\n",
|
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+
"262/1079\n",
|
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+
"263/1079\n",
|
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+
"264/1079\n",
|
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+
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|
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+
"266/1079\n",
|
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|
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+
"268/1079\n",
|
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+
"269/1079\n",
|
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+
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|
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+
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|
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|
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|
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|
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+
"280/1079\n",
|
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"281/1079\n",
|
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+
"282/1079\n",
|
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+
"283/1079\n",
|
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+
"284/1079\n",
|
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+
"285/1079\n",
|
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+
"286/1079\n",
|
| 359 |
+
"287/1079\n",
|
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+
"288/1079\n",
|
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+
"289/1079\n",
|
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+
"290/1079\n",
|
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+
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|
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+
"292/1079\n",
|
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+
"293/1079\n",
|
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+
"294/1079\n",
|
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+
"295/1079\n",
|
| 368 |
+
"296/1079\n",
|
| 369 |
+
"297/1079\n",
|
| 370 |
+
"298/1079\n",
|
| 371 |
+
"299/1079\n",
|
| 372 |
+
"300/1079\n",
|
| 373 |
+
"301/1079\n",
|
| 374 |
+
"302/1079\n",
|
| 375 |
+
"303/1079\n",
|
| 376 |
+
"304/1079\n",
|
| 377 |
+
"305/1079\n",
|
| 378 |
+
"306/1079\n",
|
| 379 |
+
"307/1079\n",
|
| 380 |
+
"308/1079\n",
|
| 381 |
+
"309/1079\n",
|
| 382 |
+
"310/1079\n",
|
| 383 |
+
"311/1079\n",
|
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+
"312/1079\n",
|
| 385 |
+
"313/1079\n",
|
| 386 |
+
"314/1079\n",
|
| 387 |
+
"315/1079\n",
|
| 388 |
+
"316/1079\n",
|
| 389 |
+
"317/1079\n",
|
| 390 |
+
"318/1079\n",
|
| 391 |
+
"319/1079\n",
|
| 392 |
+
"320/1079\n",
|
| 393 |
+
"321/1079\n",
|
| 394 |
+
"322/1079\n",
|
| 395 |
+
"323/1079\n",
|
| 396 |
+
"324/1079\n",
|
| 397 |
+
"325/1079\n",
|
| 398 |
+
"326/1079\n",
|
| 399 |
+
"327/1079\n",
|
| 400 |
+
"328/1079\n",
|
| 401 |
+
"329/1079\n",
|
| 402 |
+
"330/1079\n",
|
| 403 |
+
"331/1079\n",
|
| 404 |
+
"332/1079\n",
|
| 405 |
+
"333/1079\n",
|
| 406 |
+
"334/1079\n",
|
| 407 |
+
"335/1079\n",
|
| 408 |
+
"336/1079\n",
|
| 409 |
+
"337/1079\n",
|
| 410 |
+
"338/1079\n",
|
| 411 |
+
"339/1079\n",
|
| 412 |
+
"340/1079\n",
|
| 413 |
+
"341/1079\n",
|
| 414 |
+
"342/1079\n",
|
| 415 |
+
"343/1079\n",
|
| 416 |
+
"344/1079\n",
|
| 417 |
+
"345/1079\n",
|
| 418 |
+
"346/1079\n",
|
| 419 |
+
"347/1079\n",
|
| 420 |
+
"348/1079\n",
|
| 421 |
+
"349/1079\n",
|
| 422 |
+
"350/1079\n",
|
| 423 |
+
"351/1079\n",
|
| 424 |
+
"352/1079\n",
|
| 425 |
+
"353/1079\n",
|
| 426 |
+
"354/1079\n",
|
| 427 |
+
"355/1079\n",
|
| 428 |
+
"356/1079\n",
|
| 429 |
+
"357/1079\n",
|
| 430 |
+
"358/1079\n",
|
| 431 |
+
"359/1079\n",
|
| 432 |
+
"360/1079\n",
|
| 433 |
+
"361/1079\n",
|
| 434 |
+
"362/1079\n",
|
| 435 |
+
"363/1079\n",
|
| 436 |
+
"364/1079\n",
|
| 437 |
+
"365/1079\n",
|
| 438 |
+
"366/1079\n",
|
| 439 |
+
"367/1079\n",
|
| 440 |
+
"368/1079\n",
|
| 441 |
+
"369/1079\n",
|
| 442 |
+
"370/1079\n",
|
| 443 |
+
"371/1079\n",
|
| 444 |
+
"372/1079\n",
|
| 445 |
+
"373/1079\n",
|
| 446 |
+
"374/1079\n",
|
| 447 |
+
"375/1079\n",
|
| 448 |
+
"376/1079\n",
|
| 449 |
+
"377/1079\n",
|
| 450 |
+
"378/1079\n",
|
| 451 |
+
"379/1079\n",
|
| 452 |
+
"380/1079\n",
|
| 453 |
+
"381/1079\n",
|
| 454 |
+
"382/1079\n",
|
| 455 |
+
"383/1079\n",
|
| 456 |
+
"384/1079\n",
|
| 457 |
+
"385/1079\n",
|
| 458 |
+
"386/1079\n",
|
| 459 |
+
"387/1079\n",
|
| 460 |
+
"388/1079\n",
|
| 461 |
+
"389/1079\n",
|
| 462 |
+
"390/1079\n",
|
| 463 |
+
"391/1079\n",
|
| 464 |
+
"392/1079\n",
|
| 465 |
+
"393/1079\n",
|
| 466 |
+
"394/1079\n",
|
| 467 |
+
"395/1079\n",
|
| 468 |
+
"396/1079\n",
|
| 469 |
+
"397/1079\n",
|
| 470 |
+
"398/1079\n",
|
| 471 |
+
"399/1079\n",
|
| 472 |
+
"400/1079\n",
|
| 473 |
+
"401/1079\n",
|
| 474 |
+
"402/1079\n",
|
| 475 |
+
"403/1079\n",
|
| 476 |
+
"404/1079\n",
|
| 477 |
+
"405/1079\n",
|
| 478 |
+
"406/1079\n",
|
| 479 |
+
"407/1079\n",
|
| 480 |
+
"408/1079\n",
|
| 481 |
+
"409/1079\n",
|
| 482 |
+
"410/1079\n",
|
| 483 |
+
"411/1079\n",
|
| 484 |
+
"412/1079\n",
|
| 485 |
+
"413/1079\n",
|
| 486 |
+
"414/1079\n",
|
| 487 |
+
"415/1079\n",
|
| 488 |
+
"416/1079\n",
|
| 489 |
+
"417/1079\n",
|
| 490 |
+
"418/1079\n",
|
| 491 |
+
"419/1079\n",
|
| 492 |
+
"420/1079\n",
|
| 493 |
+
"421/1079\n",
|
| 494 |
+
"422/1079\n",
|
| 495 |
+
"423/1079\n",
|
| 496 |
+
"424/1079\n",
|
| 497 |
+
"425/1079\n",
|
| 498 |
+
"426/1079\n",
|
| 499 |
+
"427/1079\n",
|
| 500 |
+
"428/1079\n",
|
| 501 |
+
"429/1079\n",
|
| 502 |
+
"430/1079\n",
|
| 503 |
+
"431/1079\n",
|
| 504 |
+
"432/1079\n",
|
| 505 |
+
"433/1079\n",
|
| 506 |
+
"434/1079\n",
|
| 507 |
+
"435/1079\n",
|
| 508 |
+
"436/1079\n",
|
| 509 |
+
"437/1079\n",
|
| 510 |
+
" subreddit created_utc score \\\n",
|
| 511 |
+
"0 Suomi 1546181040 57 \n",
|
| 512 |
+
"1 Suomi 1546181271 15 \n",
|
| 513 |
+
"2 Suomi 1546181411 9 \n",
|
| 514 |
+
"3 Suomi 1546181411 0 \n",
|
| 515 |
+
"4 Suomi 1546181804 1 \n",
|
| 516 |
+
"\n",
|
| 517 |
+
" body predicted_language \\\n",
|
| 518 |
+
"0 Kylläpä Suomi törkeästi provosoi Venäjää. Onne... __label__fi \n",
|
| 519 |
+
"1 Vittu! Mun verorahoilla taas paskaa ostettu. O... __label__fi \n",
|
| 520 |
+
"2 Mutta ajattelitteko ollenkaan luontoa ennen ku... __label__fi \n",
|
| 521 |
+
"3 Sekin olis kova.\\nDas Boot u-612 liian legend... __label__fi \n",
|
| 522 |
+
"4 Voisi kieltää kaikkien henkeen vedettävien ain... __label__fi \n",
|
| 523 |
+
"\n",
|
| 524 |
+
" probability year day month time \n",
|
| 525 |
+
"0 0.998374 2018 30 12 14:44:00 \n",
|
| 526 |
+
"1 0.999526 2018 30 12 14:47:51 \n",
|
| 527 |
+
"2 0.989323 2018 30 12 14:50:11 \n",
|
| 528 |
+
"3 0.895473 2018 30 12 14:50:11 \n",
|
| 529 |
+
"4 0.998957 2018 30 12 14:56:44 \n",
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]
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}
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+
],
|
| 1175 |
+
"source": [
|
| 1176 |
+
"for i, filepath in enumerate(filepaths_all):\n",
|
| 1177 |
+
" print(f\"{i+1}/{len(filepaths_all)}\")\n",
|
| 1178 |
+
" try:\n",
|
| 1179 |
+
" if i == 0:\n",
|
| 1180 |
+
" df_all = pd.read_parquet(filepath)\n",
|
| 1181 |
+
" df_all = df_all.apply(lambda row: add_year_month_time_of_day(row), axis=1)\n",
|
| 1182 |
+
" else:\n",
|
| 1183 |
+
" df_new = pd.read_parquet(filepath)\n",
|
| 1184 |
+
" df_new = df_new.apply(lambda row: add_year_month_time_of_day(row), axis=1)\n",
|
| 1185 |
+
" df_all = pd.concat([df_new, df_all])\n",
|
| 1186 |
+
" except Exception as e:\n",
|
| 1187 |
+
" print(df_new.head())"
|
| 1188 |
+
]
|
| 1189 |
+
},
|
| 1190 |
+
{
|
| 1191 |
+
"cell_type": "code",
|
| 1192 |
+
"execution_count": 29,
|
| 1193 |
+
"metadata": {},
|
| 1194 |
+
"outputs": [],
|
| 1195 |
+
"source": [
|
| 1196 |
+
"df_all.to_csv('data_all_fi.csv')"
|
| 1197 |
+
]
|
| 1198 |
+
},
|
| 1199 |
+
{
|
| 1200 |
+
"cell_type": "code",
|
| 1201 |
+
"execution_count": 30,
|
| 1202 |
+
"metadata": {},
|
| 1203 |
+
"outputs": [
|
| 1204 |
+
{
|
| 1205 |
+
"data": {
|
| 1206 |
+
"text/plain": [
|
| 1207 |
+
"4476667"
|
| 1208 |
+
]
|
| 1209 |
+
},
|
| 1210 |
+
"execution_count": 30,
|
| 1211 |
+
"metadata": {},
|
| 1212 |
+
"output_type": "execute_result"
|
| 1213 |
+
}
|
| 1214 |
+
],
|
| 1215 |
+
"source": [
|
| 1216 |
+
"len(df_all)"
|
| 1217 |
+
]
|
| 1218 |
+
},
|
| 1219 |
+
{
|
| 1220 |
+
"cell_type": "code",
|
| 1221 |
+
"execution_count": 3,
|
| 1222 |
+
"metadata": {},
|
| 1223 |
+
"outputs": [],
|
| 1224 |
+
"source": [
|
| 1225 |
+
"from datasets import load_dataset"
|
| 1226 |
+
]
|
| 1227 |
+
},
|
| 1228 |
+
{
|
| 1229 |
+
"cell_type": "code",
|
| 1230 |
+
"execution_count": 5,
|
| 1231 |
+
"metadata": {},
|
| 1232 |
+
"outputs": [
|
| 1233 |
+
{
|
| 1234 |
+
"name": "stderr",
|
| 1235 |
+
"output_type": "stream",
|
| 1236 |
+
"text": [
|
| 1237 |
+
"Using custom data configuration .-e14a2d6b4b35a498\n"
|
| 1238 |
+
]
|
| 1239 |
+
},
|
| 1240 |
+
{
|
| 1241 |
+
"name": "stdout",
|
| 1242 |
+
"output_type": "stream",
|
| 1243 |
+
"text": [
|
| 1244 |
+
"Downloading and preparing dataset csv/. to G:/hf_cache/csv/.-e14a2d6b4b35a498/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"
|
| 1245 |
+
]
|
| 1246 |
+
},
|
| 1247 |
+
{
|
| 1248 |
+
"data": {
|
| 1249 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 1250 |
+
"model_id": "73a198a7d22b4a95af5a5b9fab487982",
|
| 1251 |
+
"version_major": 2,
|
| 1252 |
+
"version_minor": 0
|
| 1253 |
+
},
|
| 1254 |
+
"text/plain": [
|
| 1255 |
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"Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]"
|
| 1256 |
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]
|
| 1257 |
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},
|
| 1258 |
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|
| 1259 |
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|
| 1260 |
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|
| 1261 |
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|
| 1262 |
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"name": "stderr",
|
| 1263 |
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"output_type": "stream",
|
| 1264 |
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"text": [
|
| 1265 |
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"Computing checksums of downloaded files. They can be used for integrity verification. You can disable this by passing ignore_verifications=True to load_dataset\n"
|
| 1266 |
+
]
|
| 1267 |
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|
| 1268 |
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| 1269 |
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| 1274 |
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| 1275 |
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"text/plain": [
|
| 1276 |
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"Computing checksums: 100%|##########| 1/1 [00:14<00:00, 14.46s/it]"
|
| 1277 |
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|
| 1278 |
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|
| 1279 |
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"metadata": {},
|
| 1280 |
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|
| 1281 |
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|
| 1288 |
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|
| 1289 |
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"text/plain": [
|
| 1290 |
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"Extracting data files: 0%| | 0/1 [00:00<?, ?it/s]"
|
| 1291 |
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]
|
| 1292 |
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},
|
| 1293 |
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|
| 1294 |
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|
| 1295 |
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| 1301 |
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|
| 1302 |
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|
| 1303 |
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|
| 1304 |
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|
| 1305 |
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]
|
| 1306 |
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},
|
| 1307 |
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"metadata": {},
|
| 1308 |
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|
| 1309 |
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|
| 1310 |
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{
|
| 1311 |
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"name": "stderr",
|
| 1312 |
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|
| 1313 |
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"text": [
|
| 1314 |
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"f:\\tools\\Anaconda3\\envs\\redditEnv\\lib\\site-packages\\datasets\\download\\streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
|
| 1315 |
+
" return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
|
| 1316 |
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]
|
| 1317 |
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},
|
| 1318 |
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{
|
| 1319 |
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"name": "stdout",
|
| 1320 |
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"output_type": "stream",
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| 1321 |
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"text": [
|
| 1322 |
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"Dataset csv downloaded and prepared to G:/hf_cache/csv/.-e14a2d6b4b35a498/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"
|
| 1323 |
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]
|
| 1324 |
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}
|
| 1325 |
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],
|
| 1326 |
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"source": [
|
| 1327 |
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"dataset = load_dataset(path= './',data_files='data_all_fi.csv', split='train')"
|
| 1328 |
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]
|
| 1329 |
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|
| 1330 |
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{
|
| 1331 |
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| 1332 |
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"execution_count": 6,
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| 1333 |
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| 1341 |
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| 1343 |
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"Pushing dataset shards to the dataset hub: 0%| | 0/4 [00:00<?, ?it/s]"
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| 1344 |
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]
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| 1345 |
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},
|
| 1346 |
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|
| 1360 |
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"Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]"
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| 1372 |
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| 1373 |
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| 1374 |
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| 1386 |
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| 1400 |
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| 1401 |
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| 1428 |
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| 1429 |
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| 1430 |
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|
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| 1442 |
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|
| 1443 |
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|
| 1444 |
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|
| 1445 |
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|
| 1446 |
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| 1452 |
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|
| 1453 |
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|
| 1454 |
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|
| 1455 |
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|
| 1456 |
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|
| 1457 |
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|
| 1458 |
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"metadata": {},
|
| 1459 |
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|
| 1460 |
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}
|
| 1461 |
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],
|
| 1462 |
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"source": [
|
| 1463 |
+
"dataset.push_to_hub(\"Finnish-NLP/Reddit_fi_2006_2022\", private=True)"
|
| 1464 |
+
]
|
| 1465 |
+
},
|
| 1466 |
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{
|
| 1467 |
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|
| 1468 |
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|
| 1469 |
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|
| 1470 |
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|
| 1471 |
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|
| 1472 |
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|
| 1473 |
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],
|
| 1474 |
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"metadata": {
|
| 1475 |
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"kernelspec": {
|
| 1476 |
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"display_name": "Python 3.9.15 ('redditEnv')",
|
| 1477 |
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"language": "python",
|
| 1478 |
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"name": "python3"
|
| 1479 |
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},
|
| 1480 |
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|
| 1481 |
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|
| 1482 |
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|
| 1483 |
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|
| 1484 |
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|
| 1485 |
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|
| 1486 |
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|
| 1487 |
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|
| 1488 |
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|
| 1489 |
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|
| 1490 |
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"version": "3.9.15"
|
| 1491 |
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},
|
| 1492 |
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"orig_nbformat": 4,
|
| 1493 |
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|
| 1494 |
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|
| 1495 |
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"hash": "ef741df2a7755d2d639440173889a3c1405e2c4dc3663c5e25a76822c200d193"
|
| 1496 |
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|
| 1497 |
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|
| 1498 |
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},
|
| 1499 |
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"nbformat": 4,
|
| 1500 |
+
"nbformat_minor": 2
|
| 1501 |
+
}
|
process_initial_and_end_fi_fiiltering.ipynb
ADDED
|
@@ -0,0 +1,1727 @@
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [
|
| 8 |
+
{
|
| 9 |
+
"name": "stderr",
|
| 10 |
+
"output_type": "stream",
|
| 11 |
+
"text": [
|
| 12 |
+
"Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"
|
| 13 |
+
]
|
| 14 |
+
}
|
| 15 |
+
],
|
| 16 |
+
"source": [
|
| 17 |
+
"import pandas as pd\n",
|
| 18 |
+
"import fasttext\n",
|
| 19 |
+
"import json\n",
|
| 20 |
+
"import polars as pl\n",
|
| 21 |
+
"\n",
|
| 22 |
+
"PRETRAINED_MODEL_PATH = 'langdetect_model/lid.176.bin'\n",
|
| 23 |
+
"model = fasttext.load_model(PRETRAINED_MODEL_PATH) "
|
| 24 |
+
]
|
| 25 |
+
},
|
| 26 |
+
{
|
| 27 |
+
"cell_type": "code",
|
| 28 |
+
"execution_count": 2,
|
| 29 |
+
"metadata": {},
|
| 30 |
+
"outputs": [],
|
| 31 |
+
"source": [
|
| 32 |
+
"def load_file(path):\n",
|
| 33 |
+
" df = pl.DataFrame(columns = ['subreddit', 'body'])\n",
|
| 34 |
+
"\n",
|
| 35 |
+
" count = 0\n",
|
| 36 |
+
" with open(path, 'r', encoding='utf-8') as file:\n",
|
| 37 |
+
" data = file.readlines()\n",
|
| 38 |
+
" \n",
|
| 39 |
+
" data = [json.loads(message) for message in data]\n",
|
| 40 |
+
" df = pd.DataFrame(data)\n",
|
| 41 |
+
" data = None\n",
|
| 42 |
+
" df = df[['subreddit', 'body']]\n",
|
| 43 |
+
" df = pl.DataFrame(df)\n",
|
| 44 |
+
" print(f'amount of rows in read file: {len(df)}')\n",
|
| 45 |
+
" df = df.unique(subset=[\"body\"])\n",
|
| 46 |
+
" print(f'unique rows in read file: {len(df)}')\n",
|
| 47 |
+
" df = df.filter((pl.col(\"body\").str.lengths() > 30))\n",
|
| 48 |
+
" print(f'unique rows with len over 30: {len(df)}')\n",
|
| 49 |
+
" #df = df.filter(pl.col(\"body\").len() > 30)\n",
|
| 50 |
+
" return df"
|
| 51 |
+
]
|
| 52 |
+
},
|
| 53 |
+
{
|
| 54 |
+
"cell_type": "code",
|
| 55 |
+
"execution_count": 3,
|
| 56 |
+
"metadata": {},
|
| 57 |
+
"outputs": [],
|
| 58 |
+
"source": [
|
| 59 |
+
"import re\n",
|
| 60 |
+
"\n",
|
| 61 |
+
"def pred_lang(row):\n",
|
| 62 |
+
" try:\n",
|
| 63 |
+
" pred = model.predict(str(re.sub('\\n', '', str(row[1]))))\n",
|
| 64 |
+
" row = row + (pred[0][0],)\n",
|
| 65 |
+
" row = row + (pred[1][0],)\n",
|
| 66 |
+
" except Exception as e:\n",
|
| 67 |
+
" row = row + ('could_not_predict',)\n",
|
| 68 |
+
" row = row + ('could_not_predict',)\n",
|
| 69 |
+
" return row\n",
|
| 70 |
+
"\n",
|
| 71 |
+
" "
|
| 72 |
+
]
|
| 73 |
+
},
|
| 74 |
+
{
|
| 75 |
+
"cell_type": "code",
|
| 76 |
+
"execution_count": null,
|
| 77 |
+
"metadata": {},
|
| 78 |
+
"outputs": [],
|
| 79 |
+
"source": [
|
| 80 |
+
"# Process year by year\n",
|
| 81 |
+
"process_years = ['2011', '2012']\n",
|
| 82 |
+
"\n",
|
| 83 |
+
"for process_year in process_years:\n",
|
| 84 |
+
" filepaths = [os.getcwd() + os.sep + process_year + os.sep + filepath for filepath in os.listdir(os.getcwd() + os.sep + process_year) if filepath.endswith('.zst') == False]\n",
|
| 85 |
+
" print(f\"Starting year: {process_year}\")\n",
|
| 86 |
+
" for i, filepath in enumerate(filepaths):\n",
|
| 87 |
+
" print(f'{i+1}/{len(filepaths)}')\n",
|
| 88 |
+
" if i == 0:\n",
|
| 89 |
+
" print(f'loading file: {filepaths[i]}')\n",
|
| 90 |
+
" df = load_file(filepaths[i])\n",
|
| 91 |
+
" df = df.apply(lambda row: pred_lang(row))\n",
|
| 92 |
+
" print(f'amount of rows in read file after filtering: {len(df)}')\n",
|
| 93 |
+
" df = df.rename({\"column_0\": \"subreddit\", \"column_1\": 'body', \"column_2\": 'label', \"column_3\": 'proba'})\n",
|
| 94 |
+
" df = df.filter(pl.col(\"label\").str.contains('fi'))\n",
|
| 95 |
+
" df = df.filter(pl.col(\"proba\") > 0.5)\n",
|
| 96 |
+
" else:\n",
|
| 97 |
+
" print(\"in else\")\n",
|
| 98 |
+
" new_df = load_file(filepaths[i])\n",
|
| 99 |
+
" new_df = new_df.apply(pred_lang)\n",
|
| 100 |
+
" new_df = new_df.rename({\"column_0\": \"subreddit\", \"column_1\": 'body', \"column_2\": 'label', \"column_3\": 'proba'})\n",
|
| 101 |
+
" new_df = new_df.filter(pl.col(\"label\").str.contains('fi'))\n",
|
| 102 |
+
" new_df = new_df.filter(pl.col(\"proba\") > 0.5)\n",
|
| 103 |
+
" print(f\"amount of new rows in file to add: {len(new_df)}\")\n",
|
| 104 |
+
" df.extend(new_df)\n",
|
| 105 |
+
" print(len(df))\n",
|
| 106 |
+
" print('\\n')\n",
|
| 107 |
+
" df.write_csv(f'processed{os.sep}{process_year}_data.csv')\n",
|
| 108 |
+
" print('\\n')\n",
|
| 109 |
+
" print('\\n')\n"
|
| 110 |
+
]
|
| 111 |
+
},
|
| 112 |
+
{
|
| 113 |
+
"cell_type": "code",
|
| 114 |
+
"execution_count": null,
|
| 115 |
+
"metadata": {},
|
| 116 |
+
"outputs": [],
|
| 117 |
+
"source": [
|
| 118 |
+
"# Process file by file\n",
|
| 119 |
+
"process_years = ['2012']\n",
|
| 120 |
+
"\n",
|
| 121 |
+
"\n",
|
| 122 |
+
"for process_year in process_years:\n",
|
| 123 |
+
" filepaths = [os.getcwd() + os.sep + process_year + os.sep + filepath for filepath in os.listdir(os.getcwd() + os.sep + process_year) if filepath.endswith('.zst') == False]\n",
|
| 124 |
+
" filepaths = filepaths[6::]\n",
|
| 125 |
+
" print(f\"Starting year: {process_year}\")\n",
|
| 126 |
+
" for i, filepath in enumerate(filepaths):\n",
|
| 127 |
+
" print(f'{i+1}/{len(filepaths)}')\n",
|
| 128 |
+
" print(f'loading file: {filepaths[i]}')\n",
|
| 129 |
+
" df = load_file(filepaths[i])\n",
|
| 130 |
+
" df = df.apply(lambda row: pred_lang(row))\n",
|
| 131 |
+
" print(f'amount of rows in read file after filtering: {len(df)}')\n",
|
| 132 |
+
" df = df.rename({\"column_0\": \"subreddit\", \"column_1\": 'body', \"column_2\": 'label', \"column_3\": 'proba'})\n",
|
| 133 |
+
" df = df.filter(pl.col(\"label\").str.contains('fi'))\n",
|
| 134 |
+
" df = df.filter(pl.col(\"proba\") > 0.5)\n",
|
| 135 |
+
" df.write_csv(f'processed{os.sep}{process_year}_{i+1}_data.csv')\n",
|
| 136 |
+
" print('\\n')\n",
|
| 137 |
+
" print('\\n')\n"
|
| 138 |
+
]
|
| 139 |
+
},
|
| 140 |
+
{
|
| 141 |
+
"cell_type": "code",
|
| 142 |
+
"execution_count": 25,
|
| 143 |
+
"metadata": {},
|
| 144 |
+
"outputs": [
|
| 145 |
+
{
|
| 146 |
+
"name": "stdout",
|
| 147 |
+
"output_type": "stream",
|
| 148 |
+
"text": [
|
| 149 |
+
"['i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-01.zst']\n",
|
| 150 |
+
"Starting year: 2022\n",
|
| 151 |
+
"1/1\n"
|
| 152 |
+
]
|
| 153 |
+
},
|
| 154 |
+
{
|
| 155 |
+
"ename": "UnicodeDecodeError",
|
| 156 |
+
"evalue": "'charmap' codec can't decode byte 0x8d in position 7292: character maps to <undefined>",
|
| 157 |
+
"output_type": "error",
|
| 158 |
+
"traceback": [
|
| 159 |
+
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
| 160 |
+
"\u001b[1;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)",
|
| 161 |
+
"Cell \u001b[1;32mIn[25], line 45\u001b[0m\n\u001b[0;32m 43\u001b[0m records \u001b[39m=\u001b[39m \u001b[39mmap\u001b[39m(json\u001b[39m.\u001b[39mloads, read_lines_from_zst_file(file))\n\u001b[0;32m 44\u001b[0m datas \u001b[39m=\u001b[39m []\n\u001b[1;32m---> 45\u001b[0m \u001b[39mfor\u001b[39;00m record \u001b[39min\u001b[39;00m records:\n\u001b[0;32m 46\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(record\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mbody\u001b[39m\u001b[39m'\u001b[39m)) \u001b[39m>\u001b[39m \u001b[39m30\u001b[39m:\n\u001b[0;32m 47\u001b[0m datas\u001b[39m.\u001b[39mappend((\u001b[39mstr\u001b[39m(record\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39msubreddit\u001b[39m\u001b[39m'\u001b[39m)), \u001b[39mstr\u001b[39m(record\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mcreated_utc\u001b[39m\u001b[39m'\u001b[39m)),\u001b[39mstr\u001b[39m(record\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mscore\u001b[39m\u001b[39m'\u001b[39m)),\u001b[39mstr\u001b[39m(record\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mbody\u001b[39m\u001b[39m'\u001b[39m))))\n",
|
| 162 |
+
"Cell \u001b[1;32mIn[25], line 19\u001b[0m, in \u001b[0;36mread_lines_from_zst_file\u001b[1;34m(zstd_file_path)\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mread_lines_from_zst_file\u001b[39m(zstd_file_path:Path):\n\u001b[0;32m 15\u001b[0m \u001b[39mwith\u001b[39;00m (\n\u001b[0;32m 16\u001b[0m zstd\u001b[39m.\u001b[39mopen(zstd_file_path, mode\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mrb\u001b[39m\u001b[39m'\u001b[39m, dctx\u001b[39m=\u001b[39mDCTX, encoding\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m'\u001b[39m, errors\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m'\u001b[39m) \u001b[39mas\u001b[39;00m zfh,\n\u001b[0;32m 17\u001b[0m io\u001b[39m.\u001b[39mTextIOWrapper(zfh) \u001b[39mas\u001b[39;00m iofh\n\u001b[0;32m 18\u001b[0m ):\n\u001b[1;32m---> 19\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m iofh:\n\u001b[0;32m 20\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 21\u001b[0m \u001b[39myield\u001b[39;00m line\n",
|
| 163 |
+
"File \u001b[1;32mf:\\tools\\Anaconda3\\envs\\redditEnv\\lib\\encodings\\cp1252.py:23\u001b[0m, in \u001b[0;36mIncrementalDecoder.decode\u001b[1;34m(self, input, final)\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecode\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m, final\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[1;32m---> 23\u001b[0m \u001b[39mreturn\u001b[39;00m codecs\u001b[39m.\u001b[39;49mcharmap_decode(\u001b[39minput\u001b[39;49m,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49merrors,decoding_table)[\u001b[39m0\u001b[39m]\n",
|
| 164 |
+
"\u001b[1;31mUnicodeDecodeError\u001b[0m: 'charmap' codec can't decode byte 0x8d in position 7292: character maps to <undefined>"
|
| 165 |
+
]
|
| 166 |
+
}
|
| 167 |
+
],
|
| 168 |
+
"source": [
|
| 169 |
+
"# In use 14 GB, 1min 46.5s\n",
|
| 170 |
+
"import pandas as pd\n",
|
| 171 |
+
"import io\n",
|
| 172 |
+
"import zstandard as zstd\n",
|
| 173 |
+
"from pathlib import Path\n",
|
| 174 |
+
"import json\n",
|
| 175 |
+
"import os\n",
|
| 176 |
+
"import sys\n",
|
| 177 |
+
"\n",
|
| 178 |
+
"virhe_count = 0\n",
|
| 179 |
+
"\n",
|
| 180 |
+
"DCTX = zstd.ZstdDecompressor(max_window_size=2**31)\n",
|
| 181 |
+
"\n",
|
| 182 |
+
"def read_lines_from_zst_file(zstd_file_path:Path):\n",
|
| 183 |
+
" with (\n",
|
| 184 |
+
" zstd.open(zstd_file_path, mode='rb', dctx=DCTX, encoding='utf-8', errors='ignore') as zfh,\n",
|
| 185 |
+
" io.TextIOWrapper(zfh) as iofh\n",
|
| 186 |
+
" ):\n",
|
| 187 |
+
" for line in iofh:\n",
|
| 188 |
+
" try:\n",
|
| 189 |
+
" yield line\n",
|
| 190 |
+
" except Exception as e:\n",
|
| 191 |
+
" virhe_count +=1\n",
|
| 192 |
+
" if virhe_count % 1000 == 0:\n",
|
| 193 |
+
" print(f'virhe_count: {virhe_count}')\n",
|
| 194 |
+
" pass\n",
|
| 195 |
+
"\n",
|
| 196 |
+
"\n",
|
| 197 |
+
"\n",
|
| 198 |
+
"process_years = ['2022']\n",
|
| 199 |
+
"file_counter = 1\n",
|
| 200 |
+
"\n",
|
| 201 |
+
"for process_year in process_years:\n",
|
| 202 |
+
" filepaths = [os.getcwd() + os.sep + process_year + os.sep + filepath for filepath in os.listdir(os.getcwd() + os.sep + process_year) if filepath.endswith('.zst')]\n",
|
| 203 |
+
" filepaths = filepaths[0:1]\n",
|
| 204 |
+
" print(filepaths)\n",
|
| 205 |
+
" \n",
|
| 206 |
+
" print(f\"Starting year: {process_year}\")\n",
|
| 207 |
+
" for i, filepath in enumerate(filepaths):\n",
|
| 208 |
+
" file_counter = 1\n",
|
| 209 |
+
" print(f'{i+1}/{len(filepaths)}')\n",
|
| 210 |
+
" file = Path(filepath)\n",
|
| 211 |
+
" records = map(json.loads, read_lines_from_zst_file(file))\n",
|
| 212 |
+
" datas = []\n",
|
| 213 |
+
" for record in records:\n",
|
| 214 |
+
" if len(record.get('body')) > 30:\n",
|
| 215 |
+
" datas.append((str(record.get('subreddit')), str(record.get('created_utc')),str(record.get('score')),str(record.get('body'))))\n",
|
| 216 |
+
" if len(datas) % 1000000 == 0:\n",
|
| 217 |
+
" print(len(datas))\n",
|
| 218 |
+
" #print(f'{sys.getsizeof(datas) / (1024 * 1024)} MegaBytes')\n",
|
| 219 |
+
" if len(datas) > 10000000:\n",
|
| 220 |
+
" df = pd.DataFrame(datas)\n",
|
| 221 |
+
" df = df.rename(columns={0:'subreddit', 1:'created_utc', 2:'score', 3:'body'})\n",
|
| 222 |
+
" df.to_parquet(f'{str(process_year) + os.sep}{filepath.split(os.sep)[-1].replace(\".zst\",\"\")}_{file_counter}.parquet')\n",
|
| 223 |
+
" file_counter +=1\n",
|
| 224 |
+
" datas = []\n",
|
| 225 |
+
" \n",
|
| 226 |
+
" df = pd.DataFrame(datas)\n",
|
| 227 |
+
" df = df.rename(columns={0:'subreddit', 1:'created_utc', 2:'score', 3:'body'})\n",
|
| 228 |
+
" df.to_parquet(f'{str(process_year) + os.sep}{filepath.split(os.sep)[-1].replace(\".zst\",\"\")}_{file_counter}.parquet') \n",
|
| 229 |
+
" \n",
|
| 230 |
+
"\n",
|
| 231 |
+
"\n",
|
| 232 |
+
"\n"
|
| 233 |
+
]
|
| 234 |
+
},
|
| 235 |
+
{
|
| 236 |
+
"cell_type": "code",
|
| 237 |
+
"execution_count": 4,
|
| 238 |
+
"metadata": {},
|
| 239 |
+
"outputs": [],
|
| 240 |
+
"source": [
|
| 241 |
+
"import re\n",
|
| 242 |
+
"\n",
|
| 243 |
+
"def pred_lang(row):\n",
|
| 244 |
+
" try:\n",
|
| 245 |
+
" pred = model.predict(str(re.sub('\\n', '', str(row[3]))))\n",
|
| 246 |
+
" row = row + (pred[0][0],)\n",
|
| 247 |
+
" row = row + (pred[1][0],)\n",
|
| 248 |
+
" except Exception as e:\n",
|
| 249 |
+
" row = row + ('could_not_predict','could_not_predict')\n",
|
| 250 |
+
" return row\n",
|
| 251 |
+
"\n",
|
| 252 |
+
"def pred_lang_pd(row):\n",
|
| 253 |
+
" try:\n",
|
| 254 |
+
" pred = model.predict(str(re.sub('\\n', '', str(row['body']))))\n",
|
| 255 |
+
" row['predicted_language'] = pred[0][0]\n",
|
| 256 |
+
" row['proba'] = pred[1][0]\n",
|
| 257 |
+
" except Exception as e:\n",
|
| 258 |
+
" row['predicted_language'] = 'could_not_predict'\n",
|
| 259 |
+
" row['proba'] = 'could_not_predict'\n",
|
| 260 |
+
" return row\n",
|
| 261 |
+
"\n"
|
| 262 |
+
]
|
| 263 |
+
},
|
| 264 |
+
{
|
| 265 |
+
"cell_type": "code",
|
| 266 |
+
"execution_count": 8,
|
| 267 |
+
"metadata": {},
|
| 268 |
+
"outputs": [
|
| 269 |
+
{
|
| 270 |
+
"name": "stdout",
|
| 271 |
+
"output_type": "stream",
|
| 272 |
+
"text": [
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| 273 |
+
"1/200\n",
|
| 274 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-01_1.parquet\n",
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| 275 |
+
"original len of read file: 10000001\n",
|
| 276 |
+
"\n",
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+
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|
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+
"\n",
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"\n",
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"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-01_12.parquet\n",
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"original len of read file: 10000001\n",
|
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"\n",
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|
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+
"\n",
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"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-01_14.parquet\n",
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+
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|
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+
"\n",
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|
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+
"\n",
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|
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+
"\n",
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|
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"\n",
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"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-01_18.parquet\n",
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+
"original len of read file: 3600265\n",
|
| 339 |
+
"\n",
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"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-01_2.parquet\n",
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+
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|
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+
"\n",
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"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-01_3.parquet\n",
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+
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|
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+
"\n",
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+
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|
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+
"\n",
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+
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|
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+
"\n",
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|
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+
"\n",
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|
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+
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+
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+
"\n",
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+
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|
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+
"\n",
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+
"original len of read file: 10000001\n",
|
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+
"\n",
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+
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|
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+
"\n",
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+
"original len of read file: 10000001\n",
|
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+
"\n",
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"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-02_12.parquet\n",
|
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"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-11_15.parquet\n",
|
| 1486 |
+
"original len of read file: 10000001\n",
|
| 1487 |
+
"\n",
|
| 1488 |
+
"\n",
|
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+
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|
| 1492 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-11_16.parquet\n",
|
| 1493 |
+
"original len of read file: 7883427\n",
|
| 1494 |
+
"\n",
|
| 1495 |
+
"\n",
|
| 1496 |
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|
| 1499 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-11_2.parquet\n",
|
| 1500 |
+
"original len of read file: 10000001\n",
|
| 1501 |
+
"\n",
|
| 1502 |
+
"\n",
|
| 1503 |
+
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+
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+
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|
| 1506 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-11_3.parquet\n",
|
| 1507 |
+
"original len of read file: 10000001\n",
|
| 1508 |
+
"\n",
|
| 1509 |
+
"\n",
|
| 1510 |
+
"\n",
|
| 1511 |
+
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| 1512 |
+
"178/200\n",
|
| 1513 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-11_4.parquet\n",
|
| 1514 |
+
"original len of read file: 10000001\n",
|
| 1515 |
+
"\n",
|
| 1516 |
+
"\n",
|
| 1517 |
+
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"179/200\n",
|
| 1520 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-11_5.parquet\n",
|
| 1521 |
+
"original len of read file: 10000001\n",
|
| 1522 |
+
"\n",
|
| 1523 |
+
"\n",
|
| 1524 |
+
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|
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+
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+
"180/200\n",
|
| 1527 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-11_6.parquet\n",
|
| 1528 |
+
"original len of read file: 10000001\n",
|
| 1529 |
+
"\n",
|
| 1530 |
+
"\n",
|
| 1531 |
+
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|
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+
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"181/200\n",
|
| 1534 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-11_7.parquet\n",
|
| 1535 |
+
"original len of read file: 10000001\n",
|
| 1536 |
+
"\n",
|
| 1537 |
+
"\n",
|
| 1538 |
+
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+
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"182/200\n",
|
| 1541 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-11_8.parquet\n",
|
| 1542 |
+
"original len of read file: 10000001\n",
|
| 1543 |
+
"\n",
|
| 1544 |
+
"\n",
|
| 1545 |
+
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+
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+
"183/200\n",
|
| 1548 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-11_9.parquet\n",
|
| 1549 |
+
"original len of read file: 10000001\n",
|
| 1550 |
+
"\n",
|
| 1551 |
+
"\n",
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| 1552 |
+
"\n",
|
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+
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+
"184/200\n",
|
| 1555 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_1.parquet\n",
|
| 1556 |
+
"original len of read file: 10000001\n",
|
| 1557 |
+
"\n",
|
| 1558 |
+
"\n",
|
| 1559 |
+
"\n",
|
| 1560 |
+
"\n",
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+
"185/200\n",
|
| 1562 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_10.parquet\n",
|
| 1563 |
+
"original len of read file: 10000001\n",
|
| 1564 |
+
"\n",
|
| 1565 |
+
"\n",
|
| 1566 |
+
"\n",
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+
"\n",
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+
"186/200\n",
|
| 1569 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_11.parquet\n",
|
| 1570 |
+
"original len of read file: 10000001\n",
|
| 1571 |
+
"\n",
|
| 1572 |
+
"\n",
|
| 1573 |
+
"\n",
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+
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+
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|
| 1576 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_12.parquet\n",
|
| 1577 |
+
"original len of read file: 10000001\n",
|
| 1578 |
+
"\n",
|
| 1579 |
+
"\n",
|
| 1580 |
+
"\n",
|
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+
"\n",
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+
"188/200\n",
|
| 1583 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_13.parquet\n",
|
| 1584 |
+
"original len of read file: 10000001\n",
|
| 1585 |
+
"\n",
|
| 1586 |
+
"\n",
|
| 1587 |
+
"\n",
|
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+
"\n",
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+
"189/200\n",
|
| 1590 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_14.parquet\n",
|
| 1591 |
+
"original len of read file: 10000001\n",
|
| 1592 |
+
"\n",
|
| 1593 |
+
"\n",
|
| 1594 |
+
"\n",
|
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+
"\n",
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+
"190/200\n",
|
| 1597 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_15.parquet\n",
|
| 1598 |
+
"original len of read file: 10000001\n",
|
| 1599 |
+
"\n",
|
| 1600 |
+
"\n",
|
| 1601 |
+
"\n",
|
| 1602 |
+
"\n",
|
| 1603 |
+
"191/200\n",
|
| 1604 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_16.parquet\n",
|
| 1605 |
+
"original len of read file: 10000001\n",
|
| 1606 |
+
"\n",
|
| 1607 |
+
"\n",
|
| 1608 |
+
"\n",
|
| 1609 |
+
"\n",
|
| 1610 |
+
"192/200\n",
|
| 1611 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_17.parquet\n",
|
| 1612 |
+
"original len of read file: 3330060\n",
|
| 1613 |
+
"\n",
|
| 1614 |
+
"\n",
|
| 1615 |
+
"\n",
|
| 1616 |
+
"\n",
|
| 1617 |
+
"193/200\n",
|
| 1618 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_2.parquet\n",
|
| 1619 |
+
"original len of read file: 10000001\n",
|
| 1620 |
+
"\n",
|
| 1621 |
+
"\n",
|
| 1622 |
+
"\n",
|
| 1623 |
+
"\n",
|
| 1624 |
+
"194/200\n",
|
| 1625 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_3.parquet\n",
|
| 1626 |
+
"original len of read file: 10000001\n",
|
| 1627 |
+
"\n",
|
| 1628 |
+
"\n",
|
| 1629 |
+
"\n",
|
| 1630 |
+
"\n",
|
| 1631 |
+
"195/200\n",
|
| 1632 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_4.parquet\n",
|
| 1633 |
+
"original len of read file: 10000001\n",
|
| 1634 |
+
"\n",
|
| 1635 |
+
"\n",
|
| 1636 |
+
"\n",
|
| 1637 |
+
"\n",
|
| 1638 |
+
"196/200\n",
|
| 1639 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_5.parquet\n",
|
| 1640 |
+
"original len of read file: 10000001\n",
|
| 1641 |
+
"\n",
|
| 1642 |
+
"\n",
|
| 1643 |
+
"\n",
|
| 1644 |
+
"\n",
|
| 1645 |
+
"197/200\n",
|
| 1646 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_6.parquet\n",
|
| 1647 |
+
"original len of read file: 10000001\n",
|
| 1648 |
+
"\n",
|
| 1649 |
+
"\n",
|
| 1650 |
+
"\n",
|
| 1651 |
+
"\n",
|
| 1652 |
+
"198/200\n",
|
| 1653 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_7.parquet\n",
|
| 1654 |
+
"original len of read file: 10000001\n",
|
| 1655 |
+
"\n",
|
| 1656 |
+
"\n",
|
| 1657 |
+
"\n",
|
| 1658 |
+
"\n",
|
| 1659 |
+
"199/200\n",
|
| 1660 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_8.parquet\n",
|
| 1661 |
+
"original len of read file: 10000001\n",
|
| 1662 |
+
"\n",
|
| 1663 |
+
"\n",
|
| 1664 |
+
"\n",
|
| 1665 |
+
"\n",
|
| 1666 |
+
"200/200\n",
|
| 1667 |
+
"loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_9.parquet\n",
|
| 1668 |
+
"original len of read file: 10000001\n",
|
| 1669 |
+
"\n",
|
| 1670 |
+
"\n",
|
| 1671 |
+
"\n",
|
| 1672 |
+
"\n"
|
| 1673 |
+
]
|
| 1674 |
+
}
|
| 1675 |
+
],
|
| 1676 |
+
"source": [
|
| 1677 |
+
"process_years = ['2022']\n",
|
| 1678 |
+
"file_counter = 1\n",
|
| 1679 |
+
"\n",
|
| 1680 |
+
"for process_year in process_years:\n",
|
| 1681 |
+
" filepaths = [os.getcwd() + os.sep + process_year + os.sep + filepath for filepath in os.listdir(os.getcwd() + os.sep + process_year) if filepath.endswith('.parquet') and 'processed' not in filepath]\n",
|
| 1682 |
+
" #filepaths = filepaths[6]\n",
|
| 1683 |
+
" filepaths_fi = [os.getcwd() + os.sep + 'finnish' + os.sep + process_year + os.sep + filepath for filepath in os.listdir(os.getcwd() + os.sep + process_year) if filepath.endswith('.parquet') and 'processed' not in filepath]\n",
|
| 1684 |
+
" #filepaths_fi = filepaths_fi[69:]\n",
|
| 1685 |
+
" for i, filepath in enumerate(filepaths):\n",
|
| 1686 |
+
" print(f'{i+1}/{len(filepaths)}')\n",
|
| 1687 |
+
" print(f'loading file: {filepaths[i]}')\n",
|
| 1688 |
+
" pl_df = pl.read_parquet(filepath)\n",
|
| 1689 |
+
" print(f'original len of read file: {len(pl_df)}')\n",
|
| 1690 |
+
" pl_df = pl_df.apply(lambda row: pred_lang(row))\n",
|
| 1691 |
+
" pl_df = pl_df.rename({'column_0': 'subreddit', 'column_1': 'created_utc', 'column_2': 'score', 'column_3': 'body', 'column_4':'predicted_language', 'column_5': 'probability'})\n",
|
| 1692 |
+
" pl_df = pl_df.filter(pl.col(\"probability\") > 0.7)\n",
|
| 1693 |
+
" pl_df.write_parquet(f'{filepath.replace(\".parquet\", \"_processed.parquet\")}')\n",
|
| 1694 |
+
" pl_df = pl_df.filter(pl.col(\"predicted_language\").str.contains('fi'))\n",
|
| 1695 |
+
" pl_df.write_parquet(f'{filepaths_fi[i].replace(\".parquet\", \"_processed.parquet\")}')\n",
|
| 1696 |
+
" print('\\n')\n",
|
| 1697 |
+
" print('\\n')"
|
| 1698 |
+
]
|
| 1699 |
+
}
|
| 1700 |
+
],
|
| 1701 |
+
"metadata": {
|
| 1702 |
+
"kernelspec": {
|
| 1703 |
+
"display_name": "Python 3.8.8 64-bit ('Anaconda3')",
|
| 1704 |
+
"language": "python",
|
| 1705 |
+
"name": "python3"
|
| 1706 |
+
},
|
| 1707 |
+
"language_info": {
|
| 1708 |
+
"codemirror_mode": {
|
| 1709 |
+
"name": "ipython",
|
| 1710 |
+
"version": 3
|
| 1711 |
+
},
|
| 1712 |
+
"file_extension": ".py",
|
| 1713 |
+
"mimetype": "text/x-python",
|
| 1714 |
+
"name": "python",
|
| 1715 |
+
"nbconvert_exporter": "python",
|
| 1716 |
+
"pygments_lexer": "ipython3",
|
| 1717 |
+
"version": "3.8.8"
|
| 1718 |
+
},
|
| 1719 |
+
"vscode": {
|
| 1720 |
+
"interpreter": {
|
| 1721 |
+
"hash": "f49206fcf84a9145e7e21228cbafa911d1ac18292303b01e865d8267a9c448f7"
|
| 1722 |
+
}
|
| 1723 |
+
}
|
| 1724 |
+
},
|
| 1725 |
+
"nbformat": 4,
|
| 1726 |
+
"nbformat_minor": 2
|
| 1727 |
+
}
|
process_zst_to_parquet_new.ipynb
ADDED
|
@@ -0,0 +1,3149 @@
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| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 17,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [],
|
| 8 |
+
"source": [
|
| 9 |
+
"process_years = ['2022']\n",
|
| 10 |
+
"file_counter = 1\n",
|
| 11 |
+
"\n",
|
| 12 |
+
"for process_year in process_years:\n",
|
| 13 |
+
" filepaths = [os.getcwd() + os.sep + process_year + os.sep + filepath for filepath in os.listdir(os.getcwd() + os.sep + process_year) if filepath.endswith('.zst')]\n",
|
| 14 |
+
" "
|
| 15 |
+
]
|
| 16 |
+
},
|
| 17 |
+
{
|
| 18 |
+
"cell_type": "code",
|
| 19 |
+
"execution_count": 19,
|
| 20 |
+
"metadata": {},
|
| 21 |
+
"outputs": [
|
| 22 |
+
{
|
| 23 |
+
"data": {
|
| 24 |
+
"text/plain": [
|
| 25 |
+
"['i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-01.zst',\n",
|
| 26 |
+
" 'i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-02.zst',\n",
|
| 27 |
+
" 'i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-03.zst',\n",
|
| 28 |
+
" 'i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-04.zst',\n",
|
| 29 |
+
" 'i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-05.zst',\n",
|
| 30 |
+
" 'i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-06.zst',\n",
|
| 31 |
+
" 'i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-07.zst',\n",
|
| 32 |
+
" 'i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-08.zst',\n",
|
| 33 |
+
" 'i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-09.zst',\n",
|
| 34 |
+
" 'i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-10.zst',\n",
|
| 35 |
+
" 'i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-11.zst',\n",
|
| 36 |
+
" 'i:\\\\NLP_Datasets\\\\Reddit\\\\2022\\\\RC_2022-12.zst']"
|
| 37 |
+
]
|
| 38 |
+
},
|
| 39 |
+
"execution_count": 19,
|
| 40 |
+
"metadata": {},
|
| 41 |
+
"output_type": "execute_result"
|
| 42 |
+
}
|
| 43 |
+
],
|
| 44 |
+
"source": [
|
| 45 |
+
"filepaths"
|
| 46 |
+
]
|
| 47 |
+
},
|
| 48 |
+
{
|
| 49 |
+
"cell_type": "code",
|
| 50 |
+
"execution_count": 4,
|
| 51 |
+
"metadata": {},
|
| 52 |
+
"outputs": [],
|
| 53 |
+
"source": [
|
| 54 |
+
"# this is an example of loading and iterating over a single file\n",
|
| 55 |
+
"\n",
|
| 56 |
+
"import zstandard\n",
|
| 57 |
+
"import os\n",
|
| 58 |
+
"import json\n",
|
| 59 |
+
"import sys\n",
|
| 60 |
+
"from datetime import datetime\n",
|
| 61 |
+
"import logging.handlers\n",
|
| 62 |
+
"from pathlib import Path\n",
|
| 63 |
+
"import pandas as pd\n",
|
| 64 |
+
"\n",
|
| 65 |
+
"log = logging.getLogger(\"bot\")\n",
|
| 66 |
+
"log.setLevel(logging.DEBUG)\n",
|
| 67 |
+
"log.addHandler(logging.StreamHandler())\n",
|
| 68 |
+
"\n",
|
| 69 |
+
"\n",
|
| 70 |
+
"def read_and_decode(reader, chunk_size, max_window_size, previous_chunk=None, bytes_read=0):\n",
|
| 71 |
+
"\tchunk = reader.read(chunk_size)\n",
|
| 72 |
+
"\tbytes_read += chunk_size\n",
|
| 73 |
+
"\tif previous_chunk is not None:\n",
|
| 74 |
+
"\t\tchunk = previous_chunk + chunk\n",
|
| 75 |
+
"\ttry:\n",
|
| 76 |
+
"\t\treturn chunk.decode()\n",
|
| 77 |
+
"\texcept UnicodeDecodeError:\n",
|
| 78 |
+
"\t\tif bytes_read > max_window_size:\n",
|
| 79 |
+
"\t\t\traise UnicodeError(f\"Unable to decode frame after reading {bytes_read:,} bytes\")\n",
|
| 80 |
+
"\t\tlog.info(f\"Decoding error with {bytes_read:,} bytes, reading another chunk\")\n",
|
| 81 |
+
"\t\treturn read_and_decode(reader, chunk_size, max_window_size, chunk, bytes_read)\n",
|
| 82 |
+
"\n",
|
| 83 |
+
"\n",
|
| 84 |
+
"def read_lines_zst(file_name):\n",
|
| 85 |
+
"\twith open(file_name, 'rb') as file_handle:\n",
|
| 86 |
+
"\t\tbuffer = ''\n",
|
| 87 |
+
"\t\treader = zstandard.ZstdDecompressor(max_window_size=2**31).stream_reader(file_handle)\n",
|
| 88 |
+
"\t\t#reader.read(40000000000)\n",
|
| 89 |
+
"\t\twhile True:\n",
|
| 90 |
+
"\t\t\tchunk = read_and_decode(reader, 2**27, (2**29) * 2)\n",
|
| 91 |
+
"\n",
|
| 92 |
+
"\t\t\tif not chunk:\n",
|
| 93 |
+
"\t\t\t\tbreak\n",
|
| 94 |
+
"\t\t\tlines = (buffer + chunk).split(\"\\n\")\n",
|
| 95 |
+
"\n",
|
| 96 |
+
"\t\t\tfor line in lines[:-1]:\n",
|
| 97 |
+
"\t\t\t\tyield line\n",
|
| 98 |
+
"\n",
|
| 99 |
+
"\t\t\tbuffer = lines[-1]\n",
|
| 100 |
+
"\n",
|
| 101 |
+
"\t\treader.close()"
|
| 102 |
+
]
|
| 103 |
+
},
|
| 104 |
+
{
|
| 105 |
+
"cell_type": "code",
|
| 106 |
+
"execution_count": 20,
|
| 107 |
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"metadata": {},
|
| 108 |
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"outputs": [
|
| 109 |
+
{
|
| 110 |
+
"name": "stdout",
|
| 111 |
+
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"text": [
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"Decoding error with 134,217,728 bytes, reading another chunk\n"
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},
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{
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]
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+
},
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+
{
|
| 360 |
+
"name": "stderr",
|
| 361 |
+
"output_type": "stream",
|
| 362 |
+
"text": [
|
| 363 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 364 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 365 |
+
]
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"name": "stdout",
|
| 369 |
+
"output_type": "stream",
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"text": [
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]
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+
},
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{
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| 397 |
+
"name": "stderr",
|
| 398 |
+
"output_type": "stream",
|
| 399 |
+
"text": [
|
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+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 401 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
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+
]
|
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+
},
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+
{
|
| 405 |
+
"name": "stdout",
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"output_type": "stream",
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"text": [
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]
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},
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+
{
|
| 479 |
+
"name": "stderr",
|
| 480 |
+
"output_type": "stream",
|
| 481 |
+
"text": [
|
| 482 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 483 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 484 |
+
]
|
| 485 |
+
},
|
| 486 |
+
{
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| 487 |
+
"name": "stdout",
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| 488 |
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"output_type": "stream",
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"text": [
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},
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{
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| 497 |
+
"name": "stderr",
|
| 498 |
+
"output_type": "stream",
|
| 499 |
+
"text": [
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| 500 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 501 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 502 |
+
]
|
| 503 |
+
},
|
| 504 |
+
{
|
| 505 |
+
"name": "stdout",
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+
"output_type": "stream",
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"text": [
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]
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},
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{
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| 545 |
+
"name": "stderr",
|
| 546 |
+
"output_type": "stream",
|
| 547 |
+
"text": [
|
| 548 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 549 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 550 |
+
]
|
| 551 |
+
},
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| 552 |
+
{
|
| 553 |
+
"name": "stdout",
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| 554 |
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"output_type": "stream",
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| 555 |
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"text": [
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"5.0M / 10M\n",
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"trying to create_parquet\n",
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"7.0M / 10M\n",
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]
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},
|
| 607 |
+
{
|
| 608 |
+
"name": "stderr",
|
| 609 |
+
"output_type": "stream",
|
| 610 |
+
"text": [
|
| 611 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 612 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 613 |
+
]
|
| 614 |
+
},
|
| 615 |
+
{
|
| 616 |
+
"name": "stdout",
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| 617 |
+
"output_type": "stream",
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+
"text": [
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"\n"
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},
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{
|
| 698 |
+
"name": "stderr",
|
| 699 |
+
"output_type": "stream",
|
| 700 |
+
"text": [
|
| 701 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 702 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
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+
]
|
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+
},
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{
|
| 706 |
+
"name": "stdout",
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{
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"name": "stderr",
|
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+
"output_type": "stream",
|
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+
"text": [
|
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+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
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+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 720 |
+
]
|
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+
},
|
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+
{
|
| 723 |
+
"name": "stdout",
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},
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{
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+
"name": "stderr",
|
| 766 |
+
"output_type": "stream",
|
| 767 |
+
"text": [
|
| 768 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 769 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
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+
]
|
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+
},
|
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{
|
| 773 |
+
"name": "stdout",
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"output_type": "stream",
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"text": [
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},
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{
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+
"name": "stderr",
|
| 818 |
+
"output_type": "stream",
|
| 819 |
+
"text": [
|
| 820 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 821 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 822 |
+
]
|
| 823 |
+
},
|
| 824 |
+
{
|
| 825 |
+
"name": "stdout",
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"output_type": "stream",
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"text": [
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]
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},
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{
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+
"name": "stderr",
|
| 897 |
+
"output_type": "stream",
|
| 898 |
+
"text": [
|
| 899 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 900 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 901 |
+
]
|
| 902 |
+
},
|
| 903 |
+
{
|
| 904 |
+
"name": "stdout",
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| 905 |
+
"output_type": "stream",
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"text": [
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|
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"trying to create_parquet\n",
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|
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]
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| 944 |
+
},
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+
{
|
| 946 |
+
"name": "stderr",
|
| 947 |
+
"output_type": "stream",
|
| 948 |
+
"text": [
|
| 949 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 950 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 951 |
+
]
|
| 952 |
+
},
|
| 953 |
+
{
|
| 954 |
+
"name": "stdout",
|
| 955 |
+
"output_type": "stream",
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| 956 |
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"text": [
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"trying to create_parquet\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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"trying to create_parquet\n",
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+
"\n",
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]
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+
},
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+
{
|
| 979 |
+
"name": "stderr",
|
| 980 |
+
"output_type": "stream",
|
| 981 |
+
"text": [
|
| 982 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 983 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 984 |
+
]
|
| 985 |
+
},
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| 986 |
+
{
|
| 987 |
+
"name": "stdout",
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| 988 |
+
"output_type": "stream",
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"text": [
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"4.0M / 10M\n",
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|
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"trying to create_parquet\n",
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]
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+
},
|
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+
{
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| 1005 |
+
"name": "stderr",
|
| 1006 |
+
"output_type": "stream",
|
| 1007 |
+
"text": [
|
| 1008 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 1009 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 1010 |
+
]
|
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"name": "stdout",
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| 1044 |
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"output_type": "stream",
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"text": [
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"Decoding error with 134,217,728 bytes, reading another chunk\n",
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"Decoding error with 134,217,728 bytes, reading another chunk\n"
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"name": "stdout",
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"name": "stderr",
|
| 1151 |
+
"output_type": "stream",
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| 1152 |
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"text": [
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+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
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+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
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},
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{
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"name": "stdout",
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"name": "stderr",
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| 1192 |
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"output_type": "stream",
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"text": [
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"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
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"Decoding error with 134,217,728 bytes, reading another chunk\n"
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},
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{
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| 1199 |
+
"name": "stdout",
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"output_type": "stream",
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"text": [
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"output_type": "stream",
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"text": [
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| 1351 |
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"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 1352 |
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"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 1353 |
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]
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"1.0M / 10M\n",
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| 1422 |
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+
{
|
| 1424 |
+
"name": "stderr",
|
| 1425 |
+
"output_type": "stream",
|
| 1426 |
+
"text": [
|
| 1427 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 1428 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 1429 |
+
]
|
| 1430 |
+
},
|
| 1431 |
+
{
|
| 1432 |
+
"name": "stdout",
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| 1433 |
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"output_type": "stream",
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"text": [
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+
{
|
| 1485 |
+
"name": "stderr",
|
| 1486 |
+
"output_type": "stream",
|
| 1487 |
+
"text": [
|
| 1488 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 1489 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 1490 |
+
]
|
| 1491 |
+
},
|
| 1492 |
+
{
|
| 1493 |
+
"name": "stdout",
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"output_type": "stream",
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"text": [
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+
},
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+
{
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| 1529 |
+
"name": "stderr",
|
| 1530 |
+
"output_type": "stream",
|
| 1531 |
+
"text": [
|
| 1532 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 1533 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 1534 |
+
]
|
| 1535 |
+
},
|
| 1536 |
+
{
|
| 1537 |
+
"name": "stdout",
|
| 1538 |
+
"output_type": "stream",
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"text": [
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"3.0M / 10M\n",
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"trying to create_parquet\n",
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"7.0M / 10M\n",
|
| 1704 |
+
"8.0M / 10M\n",
|
| 1705 |
+
"9.0M / 10M\n",
|
| 1706 |
+
"10.0M / 10M\n",
|
| 1707 |
+
"trying to create_parquet\n",
|
| 1708 |
+
"\n",
|
| 1709 |
+
"1.0M / 10M\n",
|
| 1710 |
+
"2.0M / 10M\n",
|
| 1711 |
+
"3.0M / 10M\n",
|
| 1712 |
+
"4.0M / 10M\n",
|
| 1713 |
+
"5.0M / 10M\n",
|
| 1714 |
+
"6.0M / 10M\n",
|
| 1715 |
+
"7.0M / 10M\n",
|
| 1716 |
+
"8.0M / 10M\n",
|
| 1717 |
+
"9.0M / 10M\n",
|
| 1718 |
+
"10.0M / 10M\n",
|
| 1719 |
+
"trying to create_parquet\n",
|
| 1720 |
+
"\n",
|
| 1721 |
+
"1.0M / 10M\n",
|
| 1722 |
+
"2.0M / 10M\n",
|
| 1723 |
+
"3.0M / 10M\n"
|
| 1724 |
+
]
|
| 1725 |
+
},
|
| 1726 |
+
{
|
| 1727 |
+
"name": "stderr",
|
| 1728 |
+
"output_type": "stream",
|
| 1729 |
+
"text": [
|
| 1730 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 1731 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 1732 |
+
]
|
| 1733 |
+
},
|
| 1734 |
+
{
|
| 1735 |
+
"name": "stdout",
|
| 1736 |
+
"output_type": "stream",
|
| 1737 |
+
"text": [
|
| 1738 |
+
"4.0M / 10M\n",
|
| 1739 |
+
"5.0M / 10M\n",
|
| 1740 |
+
"6.0M / 10M\n",
|
| 1741 |
+
"7.0M / 10M\n",
|
| 1742 |
+
"8.0M / 10M\n",
|
| 1743 |
+
"9.0M / 10M\n",
|
| 1744 |
+
"10.0M / 10M\n",
|
| 1745 |
+
"trying to create_parquet\n",
|
| 1746 |
+
"\n",
|
| 1747 |
+
"1.0M / 10M\n",
|
| 1748 |
+
"2.0M / 10M\n",
|
| 1749 |
+
"3.0M / 10M\n",
|
| 1750 |
+
"4.0M / 10M\n",
|
| 1751 |
+
"5.0M / 10M\n",
|
| 1752 |
+
"6.0M / 10M\n",
|
| 1753 |
+
"7.0M / 10M\n",
|
| 1754 |
+
"8.0M / 10M\n",
|
| 1755 |
+
"9.0M / 10M\n",
|
| 1756 |
+
"10.0M / 10M\n",
|
| 1757 |
+
"trying to create_parquet\n",
|
| 1758 |
+
"\n",
|
| 1759 |
+
"1.0M / 10M\n",
|
| 1760 |
+
"2.0M / 10M\n",
|
| 1761 |
+
"3.0M / 10M\n",
|
| 1762 |
+
"4.0M / 10M\n",
|
| 1763 |
+
"5.0M / 10M\n",
|
| 1764 |
+
"6.0M / 10M\n",
|
| 1765 |
+
"7.0M / 10M\n",
|
| 1766 |
+
"8.0M / 10M\n",
|
| 1767 |
+
"9.0M / 10M\n",
|
| 1768 |
+
"10.0M / 10M\n",
|
| 1769 |
+
"trying to create_parquet\n",
|
| 1770 |
+
"\n",
|
| 1771 |
+
"1.0M / 10M\n",
|
| 1772 |
+
"2.0M / 10M\n",
|
| 1773 |
+
"3.0M / 10M\n",
|
| 1774 |
+
"4.0M / 10M\n",
|
| 1775 |
+
"5.0M / 10M\n",
|
| 1776 |
+
"6.0M / 10M\n",
|
| 1777 |
+
"7.0M / 10M\n",
|
| 1778 |
+
"8.0M / 10M\n",
|
| 1779 |
+
"9.0M / 10M\n",
|
| 1780 |
+
"10.0M / 10M\n",
|
| 1781 |
+
"trying to create_parquet\n",
|
| 1782 |
+
"\n",
|
| 1783 |
+
"1.0M / 10M\n",
|
| 1784 |
+
"2.0M / 10M\n",
|
| 1785 |
+
"3.0M / 10M\n",
|
| 1786 |
+
"4.0M / 10M\n",
|
| 1787 |
+
"5.0M / 10M\n",
|
| 1788 |
+
"6.0M / 10M\n",
|
| 1789 |
+
"7.0M / 10M\n",
|
| 1790 |
+
"8/12\n",
|
| 1791 |
+
"1.0M / 10M\n",
|
| 1792 |
+
"2.0M / 10M\n",
|
| 1793 |
+
"3.0M / 10M\n",
|
| 1794 |
+
"4.0M / 10M\n",
|
| 1795 |
+
"5.0M / 10M\n",
|
| 1796 |
+
"6.0M / 10M\n",
|
| 1797 |
+
"7.0M / 10M\n",
|
| 1798 |
+
"8.0M / 10M\n",
|
| 1799 |
+
"9.0M / 10M\n",
|
| 1800 |
+
"10.0M / 10M\n",
|
| 1801 |
+
"trying to create_parquet\n",
|
| 1802 |
+
"\n",
|
| 1803 |
+
"1.0M / 10M\n",
|
| 1804 |
+
"2.0M / 10M\n",
|
| 1805 |
+
"3.0M / 10M\n",
|
| 1806 |
+
"4.0M / 10M\n",
|
| 1807 |
+
"5.0M / 10M\n",
|
| 1808 |
+
"6.0M / 10M\n",
|
| 1809 |
+
"7.0M / 10M\n",
|
| 1810 |
+
"8.0M / 10M\n",
|
| 1811 |
+
"9.0M / 10M\n",
|
| 1812 |
+
"10.0M / 10M\n",
|
| 1813 |
+
"trying to create_parquet\n",
|
| 1814 |
+
"\n",
|
| 1815 |
+
"1.0M / 10M\n",
|
| 1816 |
+
"2.0M / 10M\n",
|
| 1817 |
+
"3.0M / 10M\n",
|
| 1818 |
+
"4.0M / 10M\n",
|
| 1819 |
+
"5.0M / 10M\n",
|
| 1820 |
+
"6.0M / 10M\n",
|
| 1821 |
+
"7.0M / 10M\n",
|
| 1822 |
+
"8.0M / 10M\n",
|
| 1823 |
+
"9.0M / 10M\n",
|
| 1824 |
+
"10.0M / 10M\n",
|
| 1825 |
+
"trying to create_parquet\n",
|
| 1826 |
+
"\n",
|
| 1827 |
+
"1.0M / 10M\n",
|
| 1828 |
+
"2.0M / 10M\n",
|
| 1829 |
+
"3.0M / 10M\n",
|
| 1830 |
+
"4.0M / 10M\n",
|
| 1831 |
+
"5.0M / 10M\n",
|
| 1832 |
+
"6.0M / 10M\n",
|
| 1833 |
+
"7.0M / 10M\n",
|
| 1834 |
+
"8.0M / 10M\n",
|
| 1835 |
+
"9.0M / 10M\n",
|
| 1836 |
+
"10.0M / 10M\n",
|
| 1837 |
+
"trying to create_parquet\n",
|
| 1838 |
+
"\n",
|
| 1839 |
+
"1.0M / 10M\n",
|
| 1840 |
+
"2.0M / 10M\n",
|
| 1841 |
+
"3.0M / 10M\n",
|
| 1842 |
+
"4.0M / 10M\n",
|
| 1843 |
+
"5.0M / 10M\n",
|
| 1844 |
+
"6.0M / 10M\n",
|
| 1845 |
+
"7.0M / 10M\n",
|
| 1846 |
+
"8.0M / 10M\n",
|
| 1847 |
+
"9.0M / 10M\n",
|
| 1848 |
+
"10.0M / 10M\n",
|
| 1849 |
+
"trying to create_parquet\n",
|
| 1850 |
+
"\n",
|
| 1851 |
+
"1.0M / 10M\n",
|
| 1852 |
+
"2.0M / 10M\n",
|
| 1853 |
+
"3.0M / 10M\n",
|
| 1854 |
+
"4.0M / 10M\n",
|
| 1855 |
+
"5.0M / 10M\n",
|
| 1856 |
+
"6.0M / 10M\n",
|
| 1857 |
+
"7.0M / 10M\n",
|
| 1858 |
+
"8.0M / 10M\n",
|
| 1859 |
+
"9.0M / 10M\n",
|
| 1860 |
+
"10.0M / 10M\n",
|
| 1861 |
+
"trying to create_parquet\n",
|
| 1862 |
+
"\n",
|
| 1863 |
+
"1.0M / 10M\n",
|
| 1864 |
+
"2.0M / 10M\n",
|
| 1865 |
+
"3.0M / 10M\n",
|
| 1866 |
+
"4.0M / 10M\n",
|
| 1867 |
+
"5.0M / 10M\n",
|
| 1868 |
+
"6.0M / 10M\n",
|
| 1869 |
+
"7.0M / 10M\n",
|
| 1870 |
+
"8.0M / 10M\n",
|
| 1871 |
+
"9.0M / 10M\n",
|
| 1872 |
+
"10.0M / 10M\n",
|
| 1873 |
+
"trying to create_parquet\n",
|
| 1874 |
+
"\n",
|
| 1875 |
+
"1.0M / 10M\n",
|
| 1876 |
+
"2.0M / 10M\n",
|
| 1877 |
+
"3.0M / 10M\n",
|
| 1878 |
+
"4.0M / 10M\n",
|
| 1879 |
+
"5.0M / 10M\n",
|
| 1880 |
+
"6.0M / 10M\n",
|
| 1881 |
+
"7.0M / 10M\n",
|
| 1882 |
+
"8.0M / 10M\n",
|
| 1883 |
+
"9.0M / 10M\n",
|
| 1884 |
+
"10.0M / 10M\n",
|
| 1885 |
+
"trying to create_parquet\n",
|
| 1886 |
+
"\n",
|
| 1887 |
+
"1.0M / 10M\n",
|
| 1888 |
+
"2.0M / 10M\n",
|
| 1889 |
+
"3.0M / 10M\n",
|
| 1890 |
+
"4.0M / 10M\n",
|
| 1891 |
+
"5.0M / 10M\n",
|
| 1892 |
+
"6.0M / 10M\n",
|
| 1893 |
+
"7.0M / 10M\n",
|
| 1894 |
+
"8.0M / 10M\n",
|
| 1895 |
+
"9.0M / 10M\n",
|
| 1896 |
+
"10.0M / 10M\n",
|
| 1897 |
+
"trying to create_parquet\n",
|
| 1898 |
+
"\n",
|
| 1899 |
+
"1.0M / 10M\n",
|
| 1900 |
+
"2.0M / 10M\n",
|
| 1901 |
+
"3.0M / 10M\n",
|
| 1902 |
+
"4.0M / 10M\n",
|
| 1903 |
+
"5.0M / 10M\n",
|
| 1904 |
+
"6.0M / 10M\n",
|
| 1905 |
+
"7.0M / 10M\n",
|
| 1906 |
+
"8.0M / 10M\n",
|
| 1907 |
+
"9.0M / 10M\n",
|
| 1908 |
+
"10.0M / 10M\n",
|
| 1909 |
+
"trying to create_parquet\n",
|
| 1910 |
+
"\n",
|
| 1911 |
+
"1.0M / 10M\n",
|
| 1912 |
+
"2.0M / 10M\n",
|
| 1913 |
+
"3.0M / 10M\n",
|
| 1914 |
+
"4.0M / 10M\n",
|
| 1915 |
+
"5.0M / 10M\n",
|
| 1916 |
+
"6.0M / 10M\n",
|
| 1917 |
+
"7.0M / 10M\n",
|
| 1918 |
+
"8.0M / 10M\n",
|
| 1919 |
+
"9.0M / 10M\n",
|
| 1920 |
+
"10.0M / 10M\n",
|
| 1921 |
+
"trying to create_parquet\n",
|
| 1922 |
+
"\n",
|
| 1923 |
+
"1.0M / 10M\n",
|
| 1924 |
+
"2.0M / 10M\n",
|
| 1925 |
+
"3.0M / 10M\n",
|
| 1926 |
+
"4.0M / 10M\n",
|
| 1927 |
+
"5.0M / 10M\n",
|
| 1928 |
+
"6.0M / 10M\n",
|
| 1929 |
+
"7.0M / 10M\n",
|
| 1930 |
+
"8.0M / 10M\n",
|
| 1931 |
+
"9.0M / 10M\n",
|
| 1932 |
+
"10.0M / 10M\n",
|
| 1933 |
+
"trying to create_parquet\n",
|
| 1934 |
+
"\n",
|
| 1935 |
+
"1.0M / 10M\n",
|
| 1936 |
+
"2.0M / 10M\n",
|
| 1937 |
+
"3.0M / 10M\n",
|
| 1938 |
+
"4.0M / 10M\n",
|
| 1939 |
+
"5.0M / 10M\n",
|
| 1940 |
+
"6.0M / 10M\n",
|
| 1941 |
+
"7.0M / 10M\n",
|
| 1942 |
+
"8.0M / 10M\n",
|
| 1943 |
+
"9.0M / 10M\n",
|
| 1944 |
+
"10.0M / 10M\n",
|
| 1945 |
+
"trying to create_parquet\n",
|
| 1946 |
+
"\n",
|
| 1947 |
+
"1.0M / 10M\n",
|
| 1948 |
+
"2.0M / 10M\n",
|
| 1949 |
+
"3.0M / 10M\n",
|
| 1950 |
+
"4.0M / 10M\n",
|
| 1951 |
+
"5.0M / 10M\n",
|
| 1952 |
+
"6.0M / 10M\n",
|
| 1953 |
+
"7.0M / 10M\n",
|
| 1954 |
+
"8.0M / 10M\n",
|
| 1955 |
+
"9.0M / 10M\n",
|
| 1956 |
+
"10.0M / 10M\n",
|
| 1957 |
+
"trying to create_parquet\n",
|
| 1958 |
+
"\n",
|
| 1959 |
+
"1.0M / 10M\n",
|
| 1960 |
+
"2.0M / 10M\n",
|
| 1961 |
+
"3.0M / 10M\n",
|
| 1962 |
+
"4.0M / 10M\n",
|
| 1963 |
+
"5.0M / 10M\n",
|
| 1964 |
+
"6.0M / 10M\n",
|
| 1965 |
+
"7.0M / 10M\n",
|
| 1966 |
+
"8.0M / 10M\n",
|
| 1967 |
+
"9.0M / 10M\n",
|
| 1968 |
+
"10.0M / 10M\n",
|
| 1969 |
+
"trying to create_parquet\n",
|
| 1970 |
+
"\n",
|
| 1971 |
+
"1.0M / 10M\n",
|
| 1972 |
+
"2.0M / 10M\n",
|
| 1973 |
+
"3.0M / 10M\n",
|
| 1974 |
+
"4.0M / 10M\n",
|
| 1975 |
+
"5.0M / 10M\n",
|
| 1976 |
+
"6.0M / 10M\n",
|
| 1977 |
+
"7.0M / 10M\n",
|
| 1978 |
+
"8.0M / 10M\n",
|
| 1979 |
+
"9.0M / 10M\n",
|
| 1980 |
+
"10.0M / 10M\n",
|
| 1981 |
+
"trying to create_parquet\n",
|
| 1982 |
+
"\n",
|
| 1983 |
+
"1.0M / 10M\n",
|
| 1984 |
+
"2.0M / 10M\n",
|
| 1985 |
+
"3.0M / 10M\n",
|
| 1986 |
+
"4.0M / 10M\n",
|
| 1987 |
+
"5.0M / 10M\n",
|
| 1988 |
+
"6.0M / 10M\n",
|
| 1989 |
+
"7.0M / 10M\n",
|
| 1990 |
+
"8.0M / 10M\n",
|
| 1991 |
+
"9.0M / 10M\n",
|
| 1992 |
+
"10.0M / 10M\n",
|
| 1993 |
+
"trying to create_parquet\n",
|
| 1994 |
+
"\n",
|
| 1995 |
+
"9/12\n",
|
| 1996 |
+
"1.0M / 10M\n",
|
| 1997 |
+
"2.0M / 10M\n",
|
| 1998 |
+
"3.0M / 10M\n",
|
| 1999 |
+
"4.0M / 10M\n",
|
| 2000 |
+
"5.0M / 10M\n",
|
| 2001 |
+
"6.0M / 10M\n",
|
| 2002 |
+
"7.0M / 10M\n",
|
| 2003 |
+
"8.0M / 10M\n",
|
| 2004 |
+
"9.0M / 10M\n",
|
| 2005 |
+
"10.0M / 10M\n",
|
| 2006 |
+
"trying to create_parquet\n",
|
| 2007 |
+
"\n",
|
| 2008 |
+
"1.0M / 10M\n",
|
| 2009 |
+
"2.0M / 10M\n",
|
| 2010 |
+
"3.0M / 10M\n",
|
| 2011 |
+
"4.0M / 10M\n",
|
| 2012 |
+
"5.0M / 10M\n",
|
| 2013 |
+
"6.0M / 10M\n",
|
| 2014 |
+
"7.0M / 10M\n",
|
| 2015 |
+
"8.0M / 10M\n",
|
| 2016 |
+
"9.0M / 10M\n",
|
| 2017 |
+
"10.0M / 10M\n",
|
| 2018 |
+
"trying to create_parquet\n",
|
| 2019 |
+
"\n",
|
| 2020 |
+
"1.0M / 10M\n",
|
| 2021 |
+
"2.0M / 10M\n",
|
| 2022 |
+
"3.0M / 10M\n",
|
| 2023 |
+
"4.0M / 10M\n",
|
| 2024 |
+
"5.0M / 10M\n",
|
| 2025 |
+
"6.0M / 10M\n",
|
| 2026 |
+
"7.0M / 10M\n",
|
| 2027 |
+
"8.0M / 10M\n",
|
| 2028 |
+
"9.0M / 10M\n",
|
| 2029 |
+
"10.0M / 10M\n",
|
| 2030 |
+
"trying to create_parquet\n",
|
| 2031 |
+
"\n"
|
| 2032 |
+
]
|
| 2033 |
+
},
|
| 2034 |
+
{
|
| 2035 |
+
"name": "stderr",
|
| 2036 |
+
"output_type": "stream",
|
| 2037 |
+
"text": [
|
| 2038 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 2039 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 2040 |
+
]
|
| 2041 |
+
},
|
| 2042 |
+
{
|
| 2043 |
+
"name": "stdout",
|
| 2044 |
+
"output_type": "stream",
|
| 2045 |
+
"text": [
|
| 2046 |
+
"1.0M / 10M\n",
|
| 2047 |
+
"2.0M / 10M\n",
|
| 2048 |
+
"3.0M / 10M\n",
|
| 2049 |
+
"4.0M / 10M\n"
|
| 2050 |
+
]
|
| 2051 |
+
},
|
| 2052 |
+
{
|
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"output_type": "stream",
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"text": [
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"Decoding error with 134,217,728 bytes, reading another chunk\n"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Decoding error with 134,217,728 bytes, reading another chunk\n"
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"output_type": "stream",
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"text": [
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"Decoding error with 134,217,728 bytes, reading another chunk\n"
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"output_type": "stream",
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"text": [
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"Decoding error with 134,217,728 bytes, reading another chunk\n"
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"trying to create_parquet\n",
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"trying to create_parquet\n",
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"7.0M / 10M\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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"trying to create_parquet\n",
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"2.0M / 10M\n",
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]
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| 2443 |
+
},
|
| 2444 |
+
{
|
| 2445 |
+
"name": "stderr",
|
| 2446 |
+
"output_type": "stream",
|
| 2447 |
+
"text": [
|
| 2448 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 2449 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 2450 |
+
]
|
| 2451 |
+
},
|
| 2452 |
+
{
|
| 2453 |
+
"name": "stdout",
|
| 2454 |
+
"output_type": "stream",
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| 2455 |
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"text": [
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"10.0M / 10M\n",
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"9.0M / 10M\n",
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"2.0M / 10M\n",
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"10.0M / 10M\n",
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"5.0M / 10M\n",
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"6.0M / 10M\n",
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"7.0M / 10M\n",
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"9.0M / 10M\n",
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"2.0M / 10M\n",
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"trying to create_parquet\n",
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"10.0M / 10M\n",
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| 2546 |
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"trying to create_parquet\n",
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| 2547 |
+
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"2.0M / 10M\n",
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"6.0M / 10M\n",
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"7.0M / 10M\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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| 2558 |
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"trying to create_parquet\n",
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| 2559 |
+
"\n",
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"1.0M / 10M\n",
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"2.0M / 10M\n",
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"3.0M / 10M\n",
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"4.0M / 10M\n",
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"5.0M / 10M\n",
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"6.0M / 10M\n",
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"7.0M / 10M\n",
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"8.0M / 10M\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
|
| 2570 |
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"trying to create_parquet\n",
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| 2571 |
+
"\n",
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"1.0M / 10M\n",
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"2.0M / 10M\n",
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"3.0M / 10M\n",
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"4.0M / 10M\n",
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"5.0M / 10M\n",
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"6.0M / 10M\n"
|
| 2578 |
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]
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| 2579 |
+
},
|
| 2580 |
+
{
|
| 2581 |
+
"name": "stderr",
|
| 2582 |
+
"output_type": "stream",
|
| 2583 |
+
"text": [
|
| 2584 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 2585 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 2586 |
+
]
|
| 2587 |
+
},
|
| 2588 |
+
{
|
| 2589 |
+
"name": "stdout",
|
| 2590 |
+
"output_type": "stream",
|
| 2591 |
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"text": [
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"7.0M / 10M\n",
|
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"8.0M / 10M\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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| 2596 |
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"trying to create_parquet\n",
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+
"\n",
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"1.0M / 10M\n",
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"2.0M / 10M\n",
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| 2600 |
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"3.0M / 10M\n",
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"4.0M / 10M\n",
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"5.0M / 10M\n",
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"6.0M / 10M\n",
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"7.0M / 10M\n",
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"8.0M / 10M\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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"trying to create_parquet\n",
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"1.0M / 10M\n",
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"2.0M / 10M\n",
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"3.0M / 10M\n",
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"5.0M / 10M\n",
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"6.0M / 10M\n",
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"7.0M / 10M\n",
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"8.0M / 10M\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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| 2620 |
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"trying to create_parquet\n",
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| 2621 |
+
"\n",
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"1.0M / 10M\n",
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"2.0M / 10M\n",
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| 2624 |
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"3.0M / 10M\n",
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"4.0M / 10M\n",
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"5.0M / 10M\n",
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"6.0M / 10M\n",
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"7.0M / 10M\n",
|
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"8.0M / 10M\n",
|
| 2630 |
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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| 2632 |
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"trying to create_parquet\n",
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| 2633 |
+
"\n",
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"1.0M / 10M\n",
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"2.0M / 10M\n",
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"3.0M / 10M\n",
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"4.0M / 10M\n",
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"5.0M / 10M\n",
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"6.0M / 10M\n",
|
| 2640 |
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"7.0M / 10M\n",
|
| 2641 |
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"8.0M / 10M\n",
|
| 2642 |
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"9.0M / 10M\n",
|
| 2643 |
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"10.0M / 10M\n",
|
| 2644 |
+
"trying to create_parquet\n",
|
| 2645 |
+
"\n",
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"1.0M / 10M\n",
|
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"2.0M / 10M\n",
|
| 2648 |
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"3.0M / 10M\n",
|
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"4.0M / 10M\n",
|
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"5.0M / 10M\n",
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"6.0M / 10M\n",
|
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"7.0M / 10M\n",
|
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"8.0M / 10M\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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| 2656 |
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"trying to create_parquet\n",
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+
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"1.0M / 10M\n",
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"2.0M / 10M\n",
|
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"3.0M / 10M\n",
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"4.0M / 10M\n",
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"5.0M / 10M\n"
|
| 2663 |
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]
|
| 2664 |
+
},
|
| 2665 |
+
{
|
| 2666 |
+
"name": "stderr",
|
| 2667 |
+
"output_type": "stream",
|
| 2668 |
+
"text": [
|
| 2669 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 2670 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 2671 |
+
]
|
| 2672 |
+
},
|
| 2673 |
+
{
|
| 2674 |
+
"name": "stdout",
|
| 2675 |
+
"output_type": "stream",
|
| 2676 |
+
"text": [
|
| 2677 |
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"6.0M / 10M\n",
|
| 2678 |
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"7.0M / 10M\n",
|
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"8.0M / 10M\n",
|
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"9.0M / 10M\n"
|
| 2681 |
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]
|
| 2682 |
+
},
|
| 2683 |
+
{
|
| 2684 |
+
"name": "stderr",
|
| 2685 |
+
"output_type": "stream",
|
| 2686 |
+
"text": [
|
| 2687 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 2688 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 2689 |
+
]
|
| 2690 |
+
},
|
| 2691 |
+
{
|
| 2692 |
+
"name": "stdout",
|
| 2693 |
+
"output_type": "stream",
|
| 2694 |
+
"text": [
|
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| 2696 |
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"trying to create_parquet\n",
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| 2697 |
+
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]
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| 2702 |
+
},
|
| 2703 |
+
{
|
| 2704 |
+
"name": "stderr",
|
| 2705 |
+
"output_type": "stream",
|
| 2706 |
+
"text": [
|
| 2707 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 2708 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 2709 |
+
]
|
| 2710 |
+
},
|
| 2711 |
+
{
|
| 2712 |
+
"name": "stdout",
|
| 2713 |
+
"output_type": "stream",
|
| 2714 |
+
"text": [
|
| 2715 |
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"4.0M / 10M\n",
|
| 2716 |
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"5.0M / 10M\n",
|
| 2717 |
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"6.0M / 10M\n",
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"7.0M / 10M\n",
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"8.0M / 10M\n",
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| 2720 |
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"9.0M / 10M\n",
|
| 2721 |
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"10.0M / 10M\n",
|
| 2722 |
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"trying to create_parquet\n",
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| 2723 |
+
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"3.0M / 10M\n",
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"4.0M / 10M\n",
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"5.0M / 10M\n",
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"6.0M / 10M\n",
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"7.0M / 10M\n",
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"8.0M / 10M\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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"trying to create_parquet\n",
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+
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"1.0M / 10M\n",
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"2.0M / 10M\n",
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"3.0M / 10M\n",
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"4.0M / 10M\n",
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"5.0M / 10M\n",
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"6.0M / 10M\n",
|
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"7.0M / 10M\n",
|
| 2743 |
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"8.0M / 10M\n",
|
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"9.0M / 10M\n",
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"10.0M / 10M\n",
|
| 2746 |
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"trying to create_parquet\n",
|
| 2747 |
+
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"2.0M / 10M\n",
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"3.0M / 10M\n",
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"4.0M / 10M\n",
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| 2752 |
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"5.0M / 10M\n",
|
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"6.0M / 10M\n",
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"7.0M / 10M\n",
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| 2755 |
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"2.0M / 10M\n",
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"4.0M / 10M\n",
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"6.0M / 10M\n",
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"trying to create_parquet\n",
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+
"\n",
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"1.0M / 10M\n",
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"2.0M / 10M\n"
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]
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| 2771 |
+
},
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+
{
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| 2773 |
+
"name": "stderr",
|
| 2774 |
+
"output_type": "stream",
|
| 2775 |
+
"text": [
|
| 2776 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 2777 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 2778 |
+
]
|
| 2779 |
+
},
|
| 2780 |
+
{
|
| 2781 |
+
"name": "stdout",
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| 2782 |
+
"output_type": "stream",
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+
"text": [
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"3.0M / 10M\n",
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"10.0M / 10M\n",
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"trying to create_parquet\n",
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"\n",
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"10.0M / 10M\n",
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"trying to create_parquet\n",
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"\n",
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"1.0M / 10M\n",
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"1.0M / 10M\n",
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"10.0M / 10M\n",
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"trying to create_parquet\n",
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+
"\n",
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"1.0M / 10M\n",
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"2.0M / 10M\n",
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"3.0M / 10M\n",
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"4.0M / 10M\n",
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"6.0M / 10M\n",
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"7.0M / 10M\n",
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"8.0M / 10M\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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| 2852 |
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"trying to create_parquet\n",
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+
"\n",
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"1.0M / 10M\n",
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"2.0M / 10M\n",
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"8.0M / 10M\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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| 2864 |
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"trying to create_parquet\n",
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+
"\n",
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"1.0M / 10M\n",
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"2.0M / 10M\n",
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"3.0M / 10M\n",
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"4.0M / 10M\n",
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"6.0M / 10M\n",
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"7.0M / 10M\n",
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"8.0M / 10M\n",
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"9.0M / 10M\n",
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"10.0M / 10M\n",
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| 2876 |
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"trying to create_parquet\n",
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+
"\n",
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"1.0M / 10M\n",
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"2.0M / 10M\n",
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"3.0M / 10M\n",
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]
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| 2885 |
+
},
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| 2886 |
+
{
|
| 2887 |
+
"name": "stderr",
|
| 2888 |
+
"output_type": "stream",
|
| 2889 |
+
"text": [
|
| 2890 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 2891 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 2892 |
+
]
|
| 2893 |
+
},
|
| 2894 |
+
{
|
| 2895 |
+
"name": "stdout",
|
| 2896 |
+
"output_type": "stream",
|
| 2897 |
+
"text": [
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| 2898 |
+
"7.0M / 10M\n",
|
| 2899 |
+
"8.0M / 10M\n",
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"9.0M / 10M\n",
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"trying to create_parquet\n",
|
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+
"\n"
|
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+
]
|
| 2905 |
+
},
|
| 2906 |
+
{
|
| 2907 |
+
"name": "stderr",
|
| 2908 |
+
"output_type": "stream",
|
| 2909 |
+
"text": [
|
| 2910 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 2911 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 2912 |
+
]
|
| 2913 |
+
},
|
| 2914 |
+
{
|
| 2915 |
+
"name": "stdout",
|
| 2916 |
+
"output_type": "stream",
|
| 2917 |
+
"text": [
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+
"1.0M / 10M\n",
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"2.0M / 10M\n",
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"3.0M / 10M\n",
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+
]
|
| 2924 |
+
},
|
| 2925 |
+
{
|
| 2926 |
+
"name": "stderr",
|
| 2927 |
+
"output_type": "stream",
|
| 2928 |
+
"text": [
|
| 2929 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n",
|
| 2930 |
+
"Decoding error with 134,217,728 bytes, reading another chunk\n"
|
| 2931 |
+
]
|
| 2932 |
+
},
|
| 2933 |
+
{
|
| 2934 |
+
"name": "stdout",
|
| 2935 |
+
"output_type": "stream",
|
| 2936 |
+
"text": [
|
| 2937 |
+
"6.0M / 10M\n",
|
| 2938 |
+
"7.0M / 10M\n",
|
| 2939 |
+
"8.0M / 10M\n",
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+
"9.0M / 10M\n",
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+
"10.0M / 10M\n",
|
| 2942 |
+
"trying to create_parquet\n",
|
| 2943 |
+
"\n",
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+
"1.0M / 10M\n",
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+
"2.0M / 10M\n",
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+
"3.0M / 10M\n",
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+
"4.0M / 10M\n",
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+
"5.0M / 10M\n",
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+
"6.0M / 10M\n",
|
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+
"7.0M / 10M\n",
|
| 2951 |
+
"8.0M / 10M\n",
|
| 2952 |
+
"9.0M / 10M\n",
|
| 2953 |
+
"10.0M / 10M\n",
|
| 2954 |
+
"trying to create_parquet\n",
|
| 2955 |
+
"\n",
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| 2956 |
+
"1.0M / 10M\n",
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| 2957 |
+
"2.0M / 10M\n",
|
| 2958 |
+
"3.0M / 10M\n",
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+
"4.0M / 10M\n",
|
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+
"5.0M / 10M\n",
|
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+
"6.0M / 10M\n",
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+
"7.0M / 10M\n",
|
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+
"8.0M / 10M\n",
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+
"9.0M / 10M\n",
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+
"10.0M / 10M\n",
|
| 2966 |
+
"trying to create_parquet\n",
|
| 2967 |
+
"\n",
|
| 2968 |
+
"1.0M / 10M\n",
|
| 2969 |
+
"2.0M / 10M\n",
|
| 2970 |
+
"3.0M / 10M\n",
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+
"4.0M / 10M\n",
|
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+
"5.0M / 10M\n",
|
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+
"6.0M / 10M\n",
|
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+
"7.0M / 10M\n",
|
| 2975 |
+
"8.0M / 10M\n",
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+
"9.0M / 10M\n",
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+
"10.0M / 10M\n",
|
| 2978 |
+
"trying to create_parquet\n",
|
| 2979 |
+
"\n",
|
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+
"1.0M / 10M\n",
|
| 2981 |
+
"2.0M / 10M\n",
|
| 2982 |
+
"3.0M / 10M\n",
|
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+
"4.0M / 10M\n",
|
| 2984 |
+
"5.0M / 10M\n",
|
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+
"6.0M / 10M\n",
|
| 2986 |
+
"7.0M / 10M\n",
|
| 2987 |
+
"8.0M / 10M\n",
|
| 2988 |
+
"9.0M / 10M\n",
|
| 2989 |
+
"10.0M / 10M\n",
|
| 2990 |
+
"trying to create_parquet\n",
|
| 2991 |
+
"\n",
|
| 2992 |
+
"1.0M / 10M\n",
|
| 2993 |
+
"2.0M / 10M\n",
|
| 2994 |
+
"3.0M / 10M\n",
|
| 2995 |
+
"4.0M / 10M\n",
|
| 2996 |
+
"5.0M / 10M\n",
|
| 2997 |
+
"6.0M / 10M\n",
|
| 2998 |
+
"7.0M / 10M\n",
|
| 2999 |
+
"8.0M / 10M\n",
|
| 3000 |
+
"9.0M / 10M\n",
|
| 3001 |
+
"10.0M / 10M\n",
|
| 3002 |
+
"trying to create_parquet\n",
|
| 3003 |
+
"\n",
|
| 3004 |
+
"1.0M / 10M\n",
|
| 3005 |
+
"2.0M / 10M\n",
|
| 3006 |
+
"3.0M / 10M\n"
|
| 3007 |
+
]
|
| 3008 |
+
}
|
| 3009 |
+
],
|
| 3010 |
+
"source": [
|
| 3011 |
+
"for i, file_path in enumerate(filepaths):\n",
|
| 3012 |
+
"\tfile_counter = 1\n",
|
| 3013 |
+
"\tprint(f'{i+1}/{len(filepaths)}')\n",
|
| 3014 |
+
"\tfile = Path(file_path)\n",
|
| 3015 |
+
"\trecords = map(json.loads, read_lines_zst(file))\n",
|
| 3016 |
+
"\tdatas = []\n",
|
| 3017 |
+
"\tfor record in records:\n",
|
| 3018 |
+
"\t\tif len(record.get('body')) > 30:\n",
|
| 3019 |
+
"\t\t\tdatas.append((str(record.get('subreddit')), str(record.get('created_utc')),str(record.get('score')),str(record.get('body'))))\n",
|
| 3020 |
+
"\t\t\tif len(datas) % 1000000 == 0:\n",
|
| 3021 |
+
"\t\t\t\tprint(f\"{len(datas)/1000000}M / 10M\")\n",
|
| 3022 |
+
"\t\t\t\t#print(f'{sys.getsizeof(datas) / (1024 * 1024)} MegaBytes')\n",
|
| 3023 |
+
"\t\tif len(datas) > 10000000:\n",
|
| 3024 |
+
"\t\t\tdf = pd.DataFrame(datas)\n",
|
| 3025 |
+
"\t\t\tdf = df.rename(columns={0:'subreddit', 1:'created_utc', 2:'score', 3:'body'})\n",
|
| 3026 |
+
"\t\t\tprint(\"trying to create_parquet\")\n",
|
| 3027 |
+
"\t\t\tdf.to_parquet(f'{str(process_year) + os.sep}{file_path.split(os.sep)[-1].replace(\".zst\",\"\")}_{file_counter}.parquet')\n",
|
| 3028 |
+
"\t\t\tfile_counter +=1\n",
|
| 3029 |
+
"\t\t\tprint()\n",
|
| 3030 |
+
"\t\t\tdatas = []\n",
|
| 3031 |
+
"\t\t\n",
|
| 3032 |
+
"\tdf = pd.DataFrame(datas)\n",
|
| 3033 |
+
"\tdf = df.rename(columns={0:'subreddit', 1:'created_utc', 2:'score', 3:'body'})\n",
|
| 3034 |
+
"\tdf.to_parquet(f'{str(process_year) + os.sep}{file_path.split(os.sep)[-1].replace(\".zst\",\"\")}_{file_counter}.parquet') \n",
|
| 3035 |
+
"\n",
|
| 3036 |
+
"\t\t"
|
| 3037 |
+
]
|
| 3038 |
+
},
|
| 3039 |
+
{
|
| 3040 |
+
"cell_type": "code",
|
| 3041 |
+
"execution_count": null,
|
| 3042 |
+
"metadata": {},
|
| 3043 |
+
"outputs": [],
|
| 3044 |
+
"source": [
|
| 3045 |
+
"# this is an example of loading and iterating over a single file\n",
|
| 3046 |
+
"\n",
|
| 3047 |
+
"import zstandard\n",
|
| 3048 |
+
"import os\n",
|
| 3049 |
+
"import json\n",
|
| 3050 |
+
"import sys\n",
|
| 3051 |
+
"from datetime import datetime\n",
|
| 3052 |
+
"import logging.handlers\n",
|
| 3053 |
+
"\n",
|
| 3054 |
+
"\n",
|
| 3055 |
+
"log = logging.getLogger(\"bot\")\n",
|
| 3056 |
+
"log.setLevel(logging.DEBUG)\n",
|
| 3057 |
+
"log.addHandler(logging.StreamHandler())\n",
|
| 3058 |
+
"\n",
|
| 3059 |
+
"\n",
|
| 3060 |
+
"def read_and_decode(reader, chunk_size, max_window_size, previous_chunk=None, bytes_read=0):\n",
|
| 3061 |
+
"\tchunk = reader.read(chunk_size)\n",
|
| 3062 |
+
"\tbytes_read += chunk_size\n",
|
| 3063 |
+
"\tif previous_chunk is not None:\n",
|
| 3064 |
+
"\t\tchunk = previous_chunk + chunk\n",
|
| 3065 |
+
"\ttry:\n",
|
| 3066 |
+
"\t\treturn chunk.decode()\n",
|
| 3067 |
+
"\texcept UnicodeDecodeError:\n",
|
| 3068 |
+
"\t\tif bytes_read > max_window_size:\n",
|
| 3069 |
+
"\t\t\traise UnicodeError(f\"Unable to decode frame after reading {bytes_read:,} bytes\")\n",
|
| 3070 |
+
"\t\tlog.info(f\"Decoding error with {bytes_read:,} bytes, reading another chunk\")\n",
|
| 3071 |
+
"\t\treturn read_and_decode(reader, chunk_size, max_window_size, chunk, bytes_read)\n",
|
| 3072 |
+
"\n",
|
| 3073 |
+
"\n",
|
| 3074 |
+
"def read_lines_zst(file_name):\n",
|
| 3075 |
+
"\twith open(file_name, 'rb') as file_handle:\n",
|
| 3076 |
+
"\t\tbuffer = ''\n",
|
| 3077 |
+
"\t\treader = zstandard.ZstdDecompressor(max_window_size=2**31).stream_reader(file_handle)\n",
|
| 3078 |
+
"\t\t#reader.read(40000000000)\n",
|
| 3079 |
+
"\t\twhile True:\n",
|
| 3080 |
+
"\t\t\tchunk = read_and_decode(reader, 2**27, (2**29) * 2)\n",
|
| 3081 |
+
"\n",
|
| 3082 |
+
"\t\t\tif not chunk:\n",
|
| 3083 |
+
"\t\t\t\tbreak\n",
|
| 3084 |
+
"\t\t\tlines = (buffer + chunk).split(\"\\n\")\n",
|
| 3085 |
+
"\n",
|
| 3086 |
+
"\t\t\tfor line in lines[:-1]:\n",
|
| 3087 |
+
"\t\t\t\tyield line, file_handle.tell()\n",
|
| 3088 |
+
"\n",
|
| 3089 |
+
"\t\t\tbuffer = lines[-1]\n",
|
| 3090 |
+
"\n",
|
| 3091 |
+
"\t\treader.close()\n",
|
| 3092 |
+
"\n",
|
| 3093 |
+
"\n",
|
| 3094 |
+
"if __name__ == \"__main__\":\n",
|
| 3095 |
+
"\tfile_path = sys.argv[1]\n",
|
| 3096 |
+
"\tfile_size = os.stat(file_path).st_size\n",
|
| 3097 |
+
"\tfile_lines = 0\n",
|
| 3098 |
+
"\tfile_bytes_processed = 0\n",
|
| 3099 |
+
"\tcreated = None\n",
|
| 3100 |
+
"\tfield = \"subreddit\"\n",
|
| 3101 |
+
"\tvalue = \"wallstreetbets\"\n",
|
| 3102 |
+
"\tbad_lines = 0\n",
|
| 3103 |
+
"\t# try:\n",
|
| 3104 |
+
"\tfor line, file_bytes_processed in read_lines_zst(file_path):\n",
|
| 3105 |
+
"\t\ttry:\n",
|
| 3106 |
+
"\t\t\tobj = json.loads(line)\n",
|
| 3107 |
+
"\t\t\tcreated = datetime.utcfromtimestamp(int(obj['created_utc']))\n",
|
| 3108 |
+
"\t\t\ttemp = obj[field] == value\n",
|
| 3109 |
+
"\t\texcept (KeyError, json.JSONDecodeError) as err:\n",
|
| 3110 |
+
"\t\t\tbad_lines += 1\n",
|
| 3111 |
+
"\t\tfile_lines += 1\n",
|
| 3112 |
+
"\t\tif file_lines % 100000 == 0:\n",
|
| 3113 |
+
"\t\t\tlog.info(f\"{created.strftime('%Y-%m-%d %H:%M:%S')} : {file_lines:,} : {bad_lines:,} : {file_bytes_processed:,}:{(file_bytes_processed / file_size) * 100:.0f}%\")\n",
|
| 3114 |
+
"\n",
|
| 3115 |
+
"\t# except Exception as err:\n",
|
| 3116 |
+
"\t# \tlog.info(err)\n",
|
| 3117 |
+
"\n",
|
| 3118 |
+
"\tlog.info(f\"Complete : {file_lines:,} : {bad_lines:,}\")"
|
| 3119 |
+
]
|
| 3120 |
+
}
|
| 3121 |
+
],
|
| 3122 |
+
"metadata": {
|
| 3123 |
+
"kernelspec": {
|
| 3124 |
+
"display_name": "Python 3.9.15 ('redditEnv')",
|
| 3125 |
+
"language": "python",
|
| 3126 |
+
"name": "python3"
|
| 3127 |
+
},
|
| 3128 |
+
"language_info": {
|
| 3129 |
+
"codemirror_mode": {
|
| 3130 |
+
"name": "ipython",
|
| 3131 |
+
"version": 3
|
| 3132 |
+
},
|
| 3133 |
+
"file_extension": ".py",
|
| 3134 |
+
"mimetype": "text/x-python",
|
| 3135 |
+
"name": "python",
|
| 3136 |
+
"nbconvert_exporter": "python",
|
| 3137 |
+
"pygments_lexer": "ipython3",
|
| 3138 |
+
"version": "3.9.15"
|
| 3139 |
+
},
|
| 3140 |
+
"orig_nbformat": 4,
|
| 3141 |
+
"vscode": {
|
| 3142 |
+
"interpreter": {
|
| 3143 |
+
"hash": "ef741df2a7755d2d639440173889a3c1405e2c4dc3663c5e25a76822c200d193"
|
| 3144 |
+
}
|
| 3145 |
+
}
|
| 3146 |
+
},
|
| 3147 |
+
"nbformat": 4,
|
| 3148 |
+
"nbformat_minor": 2
|
| 3149 |
+
}
|
unzip_files.ipynb
ADDED
|
@@ -0,0 +1,312 @@
|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
|
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|
|
|
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|
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|
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|
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|
|
|
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|
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|
|
|
|
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|
|
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|
|
|
|
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|
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|
|
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|
|
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|
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|
|
|
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|
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|
|
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|
|
|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
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|
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|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
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|
|
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|
|
|
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|
|
|
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|
|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [
|
| 8 |
+
{
|
| 9 |
+
"name": "stdout",
|
| 10 |
+
"output_type": "stream",
|
| 11 |
+
"text": [
|
| 12 |
+
"4 10\n",
|
| 13 |
+
"10 4\n"
|
| 14 |
+
]
|
| 15 |
+
}
|
| 16 |
+
],
|
| 17 |
+
"source": [
|
| 18 |
+
"for a in range(1, 54):\n",
|
| 19 |
+
" for b in range(1, 54):\n",
|
| 20 |
+
" if a + a * b + b == 54:\n",
|
| 21 |
+
" print(a, b)"
|
| 22 |
+
]
|
| 23 |
+
},
|
| 24 |
+
{
|
| 25 |
+
"cell_type": "code",
|
| 26 |
+
"execution_count": 6,
|
| 27 |
+
"metadata": {},
|
| 28 |
+
"outputs": [],
|
| 29 |
+
"source": [
|
| 30 |
+
"import os\n",
|
| 31 |
+
"folder_to_process = '2007'\n",
|
| 32 |
+
"\n",
|
| 33 |
+
"\n",
|
| 34 |
+
"paths = [os.getcwd() + os.sep + folder_to_process + os.sep + path for path in os.listdir(os.getcwd() + os.sep + folder_to_process) if path.endswith('.zst')]"
|
| 35 |
+
]
|
| 36 |
+
},
|
| 37 |
+
{
|
| 38 |
+
"cell_type": "code",
|
| 39 |
+
"execution_count": 7,
|
| 40 |
+
"metadata": {},
|
| 41 |
+
"outputs": [
|
| 42 |
+
{
|
| 43 |
+
"name": "stderr",
|
| 44 |
+
"output_type": "stream",
|
| 45 |
+
"text": [
|
| 46 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-01.zst: 47009336 bytes \n",
|
| 47 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-02.zst: 54750951 bytes \n",
|
| 48 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-03.zst: 62820356 bytes \n",
|
| 49 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-04.zst: 69786867 bytes \n",
|
| 50 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-05.zst: 94461864 bytes \n",
|
| 51 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-06.zst: 98423333 bytes \n",
|
| 52 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-07.zst: 112766139 bytes \n",
|
| 53 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-08.zst: 122574379 bytes \n",
|
| 54 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-09.zst: 142766226 bytes \n",
|
| 55 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-10.zst: 151656689 bytes \n",
|
| 56 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-11.zst: 210899837 bytes \n",
|
| 57 |
+
"/mnt/i/NLP_Datasets/Reddit/2007/RC_2007-12.zst: 214817048 bytes \n"
|
| 58 |
+
]
|
| 59 |
+
}
|
| 60 |
+
],
|
| 61 |
+
"source": [
|
| 62 |
+
"for path in paths:\n",
|
| 63 |
+
" os.system(f'unzstd -f {path} --memory=2048MB')"
|
| 64 |
+
]
|
| 65 |
+
},
|
| 66 |
+
{
|
| 67 |
+
"cell_type": "code",
|
| 68 |
+
"execution_count": 8,
|
| 69 |
+
"metadata": {},
|
| 70 |
+
"outputs": [],
|
| 71 |
+
"source": [
|
| 72 |
+
"import os\n",
|
| 73 |
+
"folder_to_process = '2008'\n",
|
| 74 |
+
"\n",
|
| 75 |
+
"\n",
|
| 76 |
+
"paths = [os.getcwd() + os.sep + folder_to_process + os.sep + path for path in os.listdir(os.getcwd() + os.sep + folder_to_process) if path.endswith('.zst')]"
|
| 77 |
+
]
|
| 78 |
+
},
|
| 79 |
+
{
|
| 80 |
+
"cell_type": "code",
|
| 81 |
+
"execution_count": 9,
|
| 82 |
+
"metadata": {},
|
| 83 |
+
"outputs": [
|
| 84 |
+
{
|
| 85 |
+
"name": "stderr",
|
| 86 |
+
"output_type": "stream",
|
| 87 |
+
"text": [
|
| 88 |
+
"/mnt/i/NLP_Datasets/Reddit/2008/RC_2008-01.zst: 263972619 bytes \n",
|
| 89 |
+
"/mnt/i/NLP_Datasets/Reddit/2008/RC_2008-02.zst: 256564276 bytes \n",
|
| 90 |
+
"/mnt/i/NLP_Datasets/Reddit/2008/RC_2008-03.zst: 267934549 bytes \n",
|
| 91 |
+
"/mnt/i/NLP_Datasets/Reddit/2008/RC_2008-04.zst: 272655574 bytes \n",
|
| 92 |
+
"/mnt/i/NLP_Datasets/Reddit/2008/RC_2008-05.zst: 310404232 bytes \n",
|
| 93 |
+
"/mnt/i/NLP_Datasets/Reddit/2008/RC_2008-06.zst: 336060719 bytes \n",
|
| 94 |
+
"/mnt/i/NLP_Datasets/Reddit/2008/RC_2008-07.zst: 346089066 bytes \n",
|
| 95 |
+
"/mnt/i/NLP_Datasets/Reddit/2008/RC_2008-10.zst: 456690506 bytes \n",
|
| 96 |
+
"/mnt/i/NLP_Datasets/Reddit/2008/RC_2008-11.zst: 454923167 bytes \n",
|
| 97 |
+
"/mnt/i/NLP_Datasets/Reddit/2008/RC_2008-12.zst: 490644703 bytes \n"
|
| 98 |
+
]
|
| 99 |
+
}
|
| 100 |
+
],
|
| 101 |
+
"source": [
|
| 102 |
+
"for path in paths:\n",
|
| 103 |
+
" os.system(f'unzstd -f {path} --memory=2048MB')"
|
| 104 |
+
]
|
| 105 |
+
},
|
| 106 |
+
{
|
| 107 |
+
"cell_type": "code",
|
| 108 |
+
"execution_count": 10,
|
| 109 |
+
"metadata": {},
|
| 110 |
+
"outputs": [],
|
| 111 |
+
"source": [
|
| 112 |
+
"import os\n",
|
| 113 |
+
"folder_to_process = '2009'\n",
|
| 114 |
+
"\n",
|
| 115 |
+
"\n",
|
| 116 |
+
"paths = [os.getcwd() + os.sep + folder_to_process + os.sep + path for path in os.listdir(os.getcwd() + os.sep + folder_to_process) if path.endswith('.zst')]"
|
| 117 |
+
]
|
| 118 |
+
},
|
| 119 |
+
{
|
| 120 |
+
"cell_type": "code",
|
| 121 |
+
"execution_count": 11,
|
| 122 |
+
"metadata": {},
|
| 123 |
+
"outputs": [
|
| 124 |
+
{
|
| 125 |
+
"name": "stderr",
|
| 126 |
+
"output_type": "stream",
|
| 127 |
+
"text": [
|
| 128 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2008-08.zst: 346626502 bytes \n",
|
| 129 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2008-09.zst: 396060313 bytes \n",
|
| 130 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-01.zst: 608871484 bytes \n",
|
| 131 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-02.zst: 549556409 bytes \n",
|
| 132 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-03.zst: 615767139 bytes \n",
|
| 133 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-04.zst: 641521564 bytes \n",
|
| 134 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-05.zst: 712627459 bytes \n",
|
| 135 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-06.zst: 749303499 bytes \n",
|
| 136 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-07.zst: 873978527 bytes \n",
|
| 137 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-08.zst: 1038515234 bytes \n",
|
| 138 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-09.zst: 1192147453 bytes \n",
|
| 139 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-10.zst: 1332958320 bytes \n",
|
| 140 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-11.zst: 1307127106 bytes \n",
|
| 141 |
+
"/mnt/i/NLP_Datasets/Reddit/2009/RC_2009-12.zst: 1505204158 bytes \n"
|
| 142 |
+
]
|
| 143 |
+
}
|
| 144 |
+
],
|
| 145 |
+
"source": [
|
| 146 |
+
"for path in paths:\n",
|
| 147 |
+
" os.system(f'unzstd -f {path} --memory=2048MB')"
|
| 148 |
+
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},
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{
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"cell_type": "code",
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| 152 |
+
"execution_count": 12,
|
| 153 |
+
"metadata": {},
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| 154 |
+
"outputs": [],
|
| 155 |
+
"source": [
|
| 156 |
+
"import os\n",
|
| 157 |
+
"folder_to_process = '2010'\n",
|
| 158 |
+
"\n",
|
| 159 |
+
"\n",
|
| 160 |
+
"paths = [os.getcwd() + os.sep + folder_to_process + os.sep + path for path in os.listdir(os.getcwd() + os.sep + folder_to_process) if path.endswith('.zst')]"
|
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+
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+
},
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{
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+
"cell_type": "code",
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"execution_count": 13,
|
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+
"metadata": {},
|
| 167 |
+
"outputs": [
|
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+
{
|
| 169 |
+
"name": "stderr",
|
| 170 |
+
"output_type": "stream",
|
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+
"text": [
|
| 172 |
+
"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-01.zst: 1695673319 bytes \n",
|
| 173 |
+
"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-02.zst: 1591797299 bytes \n",
|
| 174 |
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"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-03.zst: 1899665475 bytes \n",
|
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"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-04.zst: 1875866199 bytes \n",
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"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-05.zst: 1904296459 bytes \n",
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"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-06.zst: 2055584210 bytes \n",
|
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+
"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-07.zst: 2358254228 bytes \n",
|
| 179 |
+
"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-08.zst: 2481119668 bytes \n",
|
| 180 |
+
"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-09.zst: 2737071492 bytes \n",
|
| 181 |
+
"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-10.zst: 2943831426 bytes \n",
|
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+
"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-11.zst: 3320232097 bytes \n",
|
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+
"/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-12.zst: 3487464031 bytes \n"
|
| 184 |
+
]
|
| 185 |
+
}
|
| 186 |
+
],
|
| 187 |
+
"source": [
|
| 188 |
+
"for path in paths:\n",
|
| 189 |
+
" os.system(f'unzstd -f {path} --memory=2048MB')"
|
| 190 |
+
]
|
| 191 |
+
},
|
| 192 |
+
{
|
| 193 |
+
"cell_type": "code",
|
| 194 |
+
"execution_count": null,
|
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+
"metadata": {},
|
| 196 |
+
"outputs": [],
|
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+
"source": []
|
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+
},
|
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{
|
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"cell_type": "code",
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+
"execution_count": 15,
|
| 202 |
+
"metadata": {},
|
| 203 |
+
"outputs": [],
|
| 204 |
+
"source": [
|
| 205 |
+
"import os\n",
|
| 206 |
+
"folder_to_process = '2011'\n",
|
| 207 |
+
"\n",
|
| 208 |
+
"\n",
|
| 209 |
+
"paths = [os.getcwd() + os.sep + folder_to_process + os.sep + path for path in os.listdir(os.getcwd() + os.sep + folder_to_process) if path.endswith('.zst')]"
|
| 210 |
+
]
|
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+
},
|
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+
{
|
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+
"cell_type": "code",
|
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+
"execution_count": 16,
|
| 215 |
+
"metadata": {},
|
| 216 |
+
"outputs": [
|
| 217 |
+
{
|
| 218 |
+
"name": "stderr",
|
| 219 |
+
"output_type": "stream",
|
| 220 |
+
"text": [
|
| 221 |
+
"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-01.zst: 3860744761 bytes \n",
|
| 222 |
+
"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-02.zst: 3724523696 bytes \n",
|
| 223 |
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"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-03.zst: 4421426090 bytes \n",
|
| 224 |
+
"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-04.zst: 4374806147 bytes \n",
|
| 225 |
+
"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-05.zst: 5074030848 bytes \n",
|
| 226 |
+
"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-06.zst: 5624078921 bytes \n",
|
| 227 |
+
"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-07.zst: 6043941589 bytes \n",
|
| 228 |
+
"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-08.zst: 7025139374 bytes \n",
|
| 229 |
+
"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-09.zst: 6942023341 bytes \n",
|
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+
"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-10.zst: 7730112702 bytes \n",
|
| 231 |
+
"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-11.zst: 7817968596 bytes \n",
|
| 232 |
+
"/mnt/i/NLP_Datasets/Reddit/2011/RC_2011-12.zst: 8311199150 bytes \n"
|
| 233 |
+
]
|
| 234 |
+
}
|
| 235 |
+
],
|
| 236 |
+
"source": [
|
| 237 |
+
"for path in paths:\n",
|
| 238 |
+
" os.system(f'unzstd -f {path} --memory=2048MB')"
|
| 239 |
+
]
|
| 240 |
+
},
|
| 241 |
+
{
|
| 242 |
+
"cell_type": "code",
|
| 243 |
+
"execution_count": 2,
|
| 244 |
+
"metadata": {},
|
| 245 |
+
"outputs": [
|
| 246 |
+
{
|
| 247 |
+
"name": "stdout",
|
| 248 |
+
"output_type": "stream",
|
| 249 |
+
"text": [
|
| 250 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2011-12.zst\n",
|
| 251 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2012-01.zst\n",
|
| 252 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2012-02.zst\n",
|
| 253 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2012-03.zst\n",
|
| 254 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2012-04.zst\n",
|
| 255 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2012-05.zst\n",
|
| 256 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2012-06.zst\n",
|
| 257 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2012-07.zst\n",
|
| 258 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2012-08.zst\n",
|
| 259 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2012-09.zst\n",
|
| 260 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2012-10.zst\n",
|
| 261 |
+
"i:\\NLP_Datasets\\Reddit\\2012\\RC_2012-11.zst\n"
|
| 262 |
+
]
|
| 263 |
+
}
|
| 264 |
+
],
|
| 265 |
+
"source": [
|
| 266 |
+
"import os\n",
|
| 267 |
+
"folder_to_process = '2012'\n",
|
| 268 |
+
"\n",
|
| 269 |
+
"\n",
|
| 270 |
+
"paths = [os.getcwd() + os.sep + folder_to_process + os.sep + path for path in os.listdir(os.getcwd() + os.sep + folder_to_process) if path.endswith('.zst')]\n",
|
| 271 |
+
"\n",
|
| 272 |
+
"for path in paths:\n",
|
| 273 |
+
" print(path)\n",
|
| 274 |
+
" os.system(f'unzstd -f {path} --memory=2048MB')"
|
| 275 |
+
]
|
| 276 |
+
},
|
| 277 |
+
{
|
| 278 |
+
"cell_type": "code",
|
| 279 |
+
"execution_count": null,
|
| 280 |
+
"metadata": {},
|
| 281 |
+
"outputs": [],
|
| 282 |
+
"source": []
|
| 283 |
+
}
|
| 284 |
+
],
|
| 285 |
+
"metadata": {
|
| 286 |
+
"kernelspec": {
|
| 287 |
+
"display_name": "Python 3.9.15 ('redditEnv')",
|
| 288 |
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"language": "python",
|
| 289 |
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"name": "python3"
|
| 290 |
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},
|
| 291 |
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"language_info": {
|
| 292 |
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"codemirror_mode": {
|
| 293 |
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"name": "ipython",
|
| 294 |
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"version": 3
|
| 295 |
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},
|
| 296 |
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"file_extension": ".py",
|
| 297 |
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"mimetype": "text/x-python",
|
| 298 |
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"name": "python",
|
| 299 |
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"nbconvert_exporter": "python",
|
| 300 |
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"pygments_lexer": "ipython3",
|
| 301 |
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"version": "3.9.15"
|
| 302 |
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},
|
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"orig_nbformat": 4,
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| 304 |
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"vscode": {
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"interpreter": {
|
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"hash": "ef741df2a7755d2d639440173889a3c1405e2c4dc3663c5e25a76822c200d193"
|
| 307 |
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}
|
| 308 |
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}
|
| 309 |
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},
|
| 310 |
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"nbformat": 4,
|
| 311 |
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"nbformat_minor": 2
|
| 312 |
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}
|