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gather_and_final_processing_finnish.ipynb ADDED
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import pandas as pd\n",
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+ "import os \n",
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+ "\n",
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+ "folders = os.listdir(os.getcwd() + os.sep + 'finnish')\n",
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+ "\n",
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+ "paths_to_folders = [os.getcwd() + os.sep + 'finnish' + os.sep + folder for folder in folders]"
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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": 9,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "filepaths_all = []\n",
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+ "for path in paths_to_folders:\n",
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+ " filepaths = [path + os.sep + file for file in os.listdir(path)]\n",
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+ " filepaths_all.extend(filepaths)"
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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": 11,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "asd = pd.read_parquet(filepaths_all[0])"
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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": 26,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import datetime\n",
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+ "def add_year_month_time_of_day(row):\n",
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+ " row['created_utc'] = int(row['created_utc'])\n",
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+ " dt_utc_native = datetime.datetime.utcfromtimestamp(row['created_utc'])\n",
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+ " row['year'] = dt_utc_native.year\n",
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+ " row['day'] = dt_utc_native.day\n",
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+ " row['month'] = dt_utc_native.month\n",
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+ " row['time'] = dt_utc_native.strftime(\"%H:%M:%S\")\n",
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+ " return row"
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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": 20,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "df = asd.apply(lambda row: add_year_month_time_of_day(row), axis=1)"
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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": 27,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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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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+ ]
1173
+ }
1174
+ ],
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
+ "Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]"
1256
+ ]
1257
+ },
1258
+ "metadata": {},
1259
+ "output_type": "display_data"
1260
+ },
1261
+ {
1262
+ "name": "stderr",
1263
+ "output_type": "stream",
1264
+ "text": [
1265
+ "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
+ },
1268
+ {
1269
+ "data": {
1270
+ "application/vnd.jupyter.widget-view+json": {
1271
+ "model_id": "00e54b096f564f29b7e202992787777c",
1272
+ "version_major": 2,
1273
+ "version_minor": 0
1274
+ },
1275
+ "text/plain": [
1276
+ "Computing checksums: 100%|##########| 1/1 [00:14<00:00, 14.46s/it]"
1277
+ ]
1278
+ },
1279
+ "metadata": {},
1280
+ "output_type": "display_data"
1281
+ },
1282
+ {
1283
+ "data": {
1284
+ "application/vnd.jupyter.widget-view+json": {
1285
+ "model_id": "796b345e120e43e6a63ac5c0f03564f5",
1286
+ "version_major": 2,
1287
+ "version_minor": 0
1288
+ },
1289
+ "text/plain": [
1290
+ "Extracting data files: 0%| | 0/1 [00:00<?, ?it/s]"
1291
+ ]
1292
+ },
1293
+ "metadata": {},
1294
+ "output_type": "display_data"
1295
+ },
1296
+ {
1297
+ "data": {
1298
+ "application/vnd.jupyter.widget-view+json": {
1299
+ "model_id": "31f5d33610af4973b456c7dbb04d2854",
1300
+ "version_major": 2,
1301
+ "version_minor": 0
1302
+ },
1303
+ "text/plain": [
1304
+ "Generating train split: 0 examples [00:00, ? examples/s]"
1305
+ ]
1306
+ },
1307
+ "metadata": {},
1308
+ "output_type": "display_data"
1309
+ },
1310
+ {
1311
+ "name": "stderr",
1312
+ "output_type": "stream",
1313
+ "text": [
1314
+ "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
+ ]
1317
+ },
1318
+ {
1319
+ "name": "stdout",
1320
+ "output_type": "stream",
1321
+ "text": [
1322
+ "Dataset csv downloaded and prepared to G:/hf_cache/csv/.-e14a2d6b4b35a498/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"
1323
+ ]
1324
+ }
1325
+ ],
1326
+ "source": [
1327
+ "dataset = load_dataset(path= './',data_files='data_all_fi.csv', split='train')"
1328
+ ]
1329
+ },
1330
+ {
1331
+ "cell_type": "code",
1332
+ "execution_count": 6,
1333
+ "metadata": {},
1334
+ "outputs": [
1335
+ {
1336
+ "data": {
1337
+ "application/vnd.jupyter.widget-view+json": {
1338
+ "model_id": "b1fc27d6c8ed400fbc65c5b7db5a0967",
1339
+ "version_major": 2,
1340
+ "version_minor": 0
1341
+ },
1342
+ "text/plain": [
1343
+ "Pushing dataset shards to the dataset hub: 0%| | 0/4 [00:00<?, ?it/s]"
1344
+ ]
1345
+ },
1346
+ "metadata": {},
1347
+ "output_type": "display_data"
1348
+ },
1349
+ {
1350
+ "data": {
1351
+ "application/vnd.jupyter.widget-view+json": {
1352
+ "model_id": "b672db855ab948709e7f4c0624d26842",
1353
+ "version_major": 2,
1354
+ "version_minor": 0
1355
+ },
1356
+ "text/plain": [
1357
+ "Creating parquet from Arrow format: 0%| | 0/1120 [00:00<?, ?ba/s]"
1358
+ ]
1359
+ },
1360
+ "metadata": {},
1361
+ "output_type": "display_data"
1362
+ },
1363
+ {
1364
+ "data": {
1365
+ "application/vnd.jupyter.widget-view+json": {
1366
+ "model_id": "11eaa5fb2e0e49ceb5fa1afa09208337",
1367
+ "version_major": 2,
1368
+ "version_minor": 0
1369
+ },
1370
+ "text/plain": [
1371
+ "Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]"
1372
+ ]
1373
+ },
1374
+ "metadata": {},
1375
+ "output_type": "display_data"
1376
+ },
1377
+ {
1378
+ "data": {
1379
+ "application/vnd.jupyter.widget-view+json": {
1380
+ "model_id": "a53c6c7c1f5942bfa064f26bc81b0721",
1381
+ "version_major": 2,
1382
+ "version_minor": 0
1383
+ },
1384
+ "text/plain": [
1385
+ "Creating parquet from Arrow format: 0%| | 0/1120 [00:00<?, ?ba/s]"
1386
+ ]
1387
+ },
1388
+ "metadata": {},
1389
+ "output_type": "display_data"
1390
+ },
1391
+ {
1392
+ "data": {
1393
+ "application/vnd.jupyter.widget-view+json": {
1394
+ "model_id": "de80a629f798442f925b54b15c74a6c8",
1395
+ "version_major": 2,
1396
+ "version_minor": 0
1397
+ },
1398
+ "text/plain": [
1399
+ "Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]"
1400
+ ]
1401
+ },
1402
+ "metadata": {},
1403
+ "output_type": "display_data"
1404
+ },
1405
+ {
1406
+ "data": {
1407
+ "application/vnd.jupyter.widget-view+json": {
1408
+ "model_id": "148324cc96804841bff0bc1ab8faaf8a",
1409
+ "version_major": 2,
1410
+ "version_minor": 0
1411
+ },
1412
+ "text/plain": [
1413
+ "Creating parquet from Arrow format: 0%| | 0/1120 [00:00<?, ?ba/s]"
1414
+ ]
1415
+ },
1416
+ "metadata": {},
1417
+ "output_type": "display_data"
1418
+ },
1419
+ {
1420
+ "data": {
1421
+ "application/vnd.jupyter.widget-view+json": {
1422
+ "model_id": "b1f067b850324ba6b01a31c81acc4213",
1423
+ "version_major": 2,
1424
+ "version_minor": 0
1425
+ },
1426
+ "text/plain": [
1427
+ "Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]"
1428
+ ]
1429
+ },
1430
+ "metadata": {},
1431
+ "output_type": "display_data"
1432
+ },
1433
+ {
1434
+ "data": {
1435
+ "application/vnd.jupyter.widget-view+json": {
1436
+ "model_id": "f21ff3f50b98420e90550d7eab50a1ee",
1437
+ "version_major": 2,
1438
+ "version_minor": 0
1439
+ },
1440
+ "text/plain": [
1441
+ "Creating parquet from Arrow format: 0%| | 0/1120 [00:00<?, ?ba/s]"
1442
+ ]
1443
+ },
1444
+ "metadata": {},
1445
+ "output_type": "display_data"
1446
+ },
1447
+ {
1448
+ "data": {
1449
+ "application/vnd.jupyter.widget-view+json": {
1450
+ "model_id": "14e2a74e795043e39a3131d7394aa70a",
1451
+ "version_major": 2,
1452
+ "version_minor": 0
1453
+ },
1454
+ "text/plain": [
1455
+ "Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]"
1456
+ ]
1457
+ },
1458
+ "metadata": {},
1459
+ "output_type": "display_data"
1460
+ }
1461
+ ],
1462
+ "source": [
1463
+ "dataset.push_to_hub(\"Finnish-NLP/Reddit_fi_2006_2022\", private=True)"
1464
+ ]
1465
+ },
1466
+ {
1467
+ "cell_type": "code",
1468
+ "execution_count": null,
1469
+ "metadata": {},
1470
+ "outputs": [],
1471
+ "source": []
1472
+ }
1473
+ ],
1474
+ "metadata": {
1475
+ "kernelspec": {
1476
+ "display_name": "Python 3.9.15 ('redditEnv')",
1477
+ "language": "python",
1478
+ "name": "python3"
1479
+ },
1480
+ "language_info": {
1481
+ "codemirror_mode": {
1482
+ "name": "ipython",
1483
+ "version": 3
1484
+ },
1485
+ "file_extension": ".py",
1486
+ "mimetype": "text/x-python",
1487
+ "name": "python",
1488
+ "nbconvert_exporter": "python",
1489
+ "pygments_lexer": "ipython3",
1490
+ "version": "3.9.15"
1491
+ },
1492
+ "orig_nbformat": 4,
1493
+ "vscode": {
1494
+ "interpreter": {
1495
+ "hash": "ef741df2a7755d2d639440173889a3c1405e2c4dc3663c5e25a76822c200d193"
1496
+ }
1497
+ }
1498
+ },
1499
+ "nbformat": 4,
1500
+ "nbformat_minor": 2
1501
+ }
process_initial_and_end_fi_fiiltering.ipynb ADDED
@@ -0,0 +1,1727 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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": [
273
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+ "loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_7.parquet\n",
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+ "loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_8.parquet\n",
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+ "loading file: i:\\NLP_Datasets\\Reddit\\2022\\RC_2022-12_9.parquet\n",
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+ "original len of read file: 10000001\n",
1669
+ "\n",
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+ "\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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ "metadata": {},
108
+ "outputs": [
109
+ {
110
+ "name": "stdout",
111
+ "output_type": "stream",
112
+ "text": [
113
+ "1/12\n",
114
+ "1.0M / 10M\n",
115
+ "2.0M / 10M\n",
116
+ "3.0M / 10M\n",
117
+ "4.0M / 10M\n",
118
+ "5.0M / 10M\n",
119
+ "6.0M / 10M\n",
120
+ "7.0M / 10M\n",
121
+ "8.0M / 10M\n",
122
+ "9.0M / 10M\n",
123
+ "10.0M / 10M\n",
124
+ "trying to create_parquet\n",
125
+ "\n",
126
+ "1.0M / 10M\n",
127
+ "2.0M / 10M\n",
128
+ "3.0M / 10M\n",
129
+ "4.0M / 10M\n",
130
+ "5.0M / 10M\n",
131
+ "6.0M / 10M\n",
132
+ "7.0M / 10M\n",
133
+ "8.0M / 10M\n",
134
+ "9.0M / 10M\n",
135
+ "10.0M / 10M\n",
136
+ "trying to create_parquet\n",
137
+ "\n",
138
+ "1.0M / 10M\n",
139
+ "2.0M / 10M\n",
140
+ "3.0M / 10M\n",
141
+ "4.0M / 10M\n",
142
+ "5.0M / 10M\n",
143
+ "6.0M / 10M\n",
144
+ "7.0M / 10M\n",
145
+ "8.0M / 10M\n",
146
+ "9.0M / 10M\n",
147
+ "10.0M / 10M\n",
148
+ "trying to create_parquet\n",
149
+ "\n",
150
+ "1.0M / 10M\n",
151
+ "2.0M / 10M\n",
152
+ "3.0M / 10M\n",
153
+ "4.0M / 10M\n",
154
+ "5.0M / 10M\n",
155
+ "6.0M / 10M\n",
156
+ "7.0M / 10M\n",
157
+ "8.0M / 10M\n",
158
+ "9.0M / 10M\n",
159
+ "10.0M / 10M\n",
160
+ "trying to create_parquet\n",
161
+ "\n",
162
+ "1.0M / 10M\n",
163
+ "2.0M / 10M\n",
164
+ "3.0M / 10M\n",
165
+ "4.0M / 10M\n",
166
+ "5.0M / 10M\n",
167
+ "6.0M / 10M\n",
168
+ "7.0M / 10M\n",
169
+ "8.0M / 10M\n",
170
+ "9.0M / 10M\n",
171
+ "10.0M / 10M\n",
172
+ "trying to create_parquet\n",
173
+ "\n",
174
+ "1.0M / 10M\n",
175
+ "2.0M / 10M\n",
176
+ "3.0M / 10M\n",
177
+ "4.0M / 10M\n",
178
+ "5.0M / 10M\n",
179
+ "6.0M / 10M\n",
180
+ "7.0M / 10M\n",
181
+ "8.0M / 10M\n",
182
+ "9.0M / 10M\n",
183
+ "10.0M / 10M\n",
184
+ "trying to create_parquet\n",
185
+ "\n",
186
+ "1.0M / 10M\n",
187
+ "2.0M / 10M\n",
188
+ "3.0M / 10M\n",
189
+ "4.0M / 10M\n",
190
+ "5.0M / 10M\n",
191
+ "6.0M / 10M\n"
192
+ ]
193
+ },
194
+ {
195
+ "name": "stderr",
196
+ "output_type": "stream",
197
+ "text": [
198
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
199
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
200
+ ]
201
+ },
202
+ {
203
+ "name": "stdout",
204
+ "output_type": "stream",
205
+ "text": [
206
+ "7.0M / 10M\n",
207
+ "8.0M / 10M\n",
208
+ "9.0M / 10M\n",
209
+ "10.0M / 10M\n",
210
+ "trying to create_parquet\n",
211
+ "\n",
212
+ "1.0M / 10M\n",
213
+ "2.0M / 10M\n",
214
+ "3.0M / 10M\n",
215
+ "4.0M / 10M\n",
216
+ "5.0M / 10M\n",
217
+ "6.0M / 10M\n",
218
+ "7.0M / 10M\n",
219
+ "8.0M / 10M\n",
220
+ "9.0M / 10M\n",
221
+ "10.0M / 10M\n",
222
+ "trying to create_parquet\n",
223
+ "\n",
224
+ "1.0M / 10M\n",
225
+ "2.0M / 10M\n",
226
+ "3.0M / 10M\n",
227
+ "4.0M / 10M\n",
228
+ "5.0M / 10M\n",
229
+ "6.0M / 10M\n",
230
+ "7.0M / 10M\n",
231
+ "8.0M / 10M\n",
232
+ "9.0M / 10M\n",
233
+ "10.0M / 10M\n",
234
+ "trying to create_parquet\n",
235
+ "\n",
236
+ "1.0M / 10M\n",
237
+ "2.0M / 10M\n",
238
+ "3.0M / 10M\n",
239
+ "4.0M / 10M\n",
240
+ "5.0M / 10M\n",
241
+ "6.0M / 10M\n",
242
+ "7.0M / 10M\n",
243
+ "8.0M / 10M\n",
244
+ "9.0M / 10M\n",
245
+ "10.0M / 10M\n",
246
+ "trying to create_parquet\n",
247
+ "\n",
248
+ "1.0M / 10M\n",
249
+ "2.0M / 10M\n",
250
+ "3.0M / 10M\n",
251
+ "4.0M / 10M\n",
252
+ "5.0M / 10M\n",
253
+ "6.0M / 10M\n",
254
+ "7.0M / 10M\n",
255
+ "8.0M / 10M\n",
256
+ "9.0M / 10M\n",
257
+ "10.0M / 10M\n",
258
+ "trying to create_parquet\n",
259
+ "\n",
260
+ "1.0M / 10M\n",
261
+ "2.0M / 10M\n"
262
+ ]
263
+ },
264
+ {
265
+ "name": "stderr",
266
+ "output_type": "stream",
267
+ "text": [
268
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
269
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
270
+ ]
271
+ },
272
+ {
273
+ "name": "stdout",
274
+ "output_type": "stream",
275
+ "text": [
276
+ "3.0M / 10M\n",
277
+ "4.0M / 10M\n",
278
+ "5.0M / 10M\n",
279
+ "6.0M / 10M\n",
280
+ "7.0M / 10M\n",
281
+ "8.0M / 10M\n",
282
+ "9.0M / 10M\n",
283
+ "10.0M / 10M\n",
284
+ "trying to create_parquet\n",
285
+ "\n",
286
+ "1.0M / 10M\n",
287
+ "2.0M / 10M\n",
288
+ "3.0M / 10M\n",
289
+ "4.0M / 10M\n",
290
+ "5.0M / 10M\n",
291
+ "6.0M / 10M\n",
292
+ "7.0M / 10M\n",
293
+ "8.0M / 10M\n",
294
+ "9.0M / 10M\n",
295
+ "10.0M / 10M\n",
296
+ "trying to create_parquet\n",
297
+ "\n",
298
+ "1.0M / 10M\n",
299
+ "2.0M / 10M\n",
300
+ "3.0M / 10M\n",
301
+ "4.0M / 10M\n",
302
+ "5.0M / 10M\n",
303
+ "6.0M / 10M\n",
304
+ "7.0M / 10M\n",
305
+ "8.0M / 10M\n",
306
+ "9.0M / 10M\n",
307
+ "10.0M / 10M\n",
308
+ "trying to create_parquet\n",
309
+ "\n",
310
+ "1.0M / 10M\n",
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+ ]
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+ },
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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",
364
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
365
+ ]
366
+ },
367
+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ ]
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+ },
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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",
401
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
402
+ ]
403
+ },
404
+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ ]
477
+ },
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+ {
479
+ "name": "stderr",
480
+ "output_type": "stream",
481
+ "text": [
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+ "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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+ "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",
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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",
501
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
502
+ ]
503
+ },
504
+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "7.0M / 10M\n",
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+ "9.0M / 10M\n",
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+ "4.0M / 10M\n"
542
+ ]
543
+ },
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+ {
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+ "name": "stderr",
546
+ "output_type": "stream",
547
+ "text": [
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+ "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
+ },
552
+ {
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+ "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",
609
+ "output_type": "stream",
610
+ "text": [
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+ "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
+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "\n"
695
+ ]
696
+ },
697
+ {
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"
703
+ ]
704
+ },
705
+ {
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+ "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",
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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",
719
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
720
+ ]
721
+ },
722
+ {
723
+ "name": "stdout",
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+ "output_type": "stream",
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+ ]
763
+ },
764
+ {
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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"
770
+ ]
771
+ },
772
+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ ]
815
+ },
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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
+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ ]
894
+ },
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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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+ "output_type": "stream",
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944
+ },
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+ {
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+ "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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976
+ ]
977
+ },
978
+ {
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
+ },
986
+ {
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+ "name": "stdout",
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+ ]
1003
+ },
1004
+ {
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
+ ]
1011
+ },
1012
+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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1040
+ ]
1041
+ },
1042
+ {
1043
+ "name": "stderr",
1044
+ "output_type": "stream",
1045
+ "text": [
1046
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
1047
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
1048
+ ]
1049
+ },
1050
+ {
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+ ]
1148
+ },
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+ {
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+ "name": "stderr",
1151
+ "output_type": "stream",
1152
+ "text": [
1153
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
1154
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
1155
+ ]
1156
+ },
1157
+ {
1158
+ "name": "stdout",
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+ "output_type": "stream",
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+ ]
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+ },
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+ {
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+ "name": "stderr",
1192
+ "output_type": "stream",
1193
+ "text": [
1194
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
1195
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
1196
+ ]
1197
+ },
1198
+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ ]
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+ },
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+ {
1348
+ "name": "stderr",
1349
+ "output_type": "stream",
1350
+ "text": [
1351
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
1352
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
1353
+ ]
1354
+ },
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+ "text": [
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+ "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
+ },
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+ {
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+ "output_type": "stream",
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+ "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
+ },
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+ {
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+ },
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+ "output_type": "stream",
1531
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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
+ },
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+ {
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+ "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
+ },
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+ {
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+ "9.0M / 10M\n",
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+ "10.0M / 10M\n",
2018
+ "trying to create_parquet\n",
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+ "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
+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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2050
+ ]
2051
+ },
2052
+ {
2053
+ "name": "stderr",
2054
+ "output_type": "stream",
2055
+ "text": [
2056
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
2057
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
2058
+ ]
2059
+ },
2060
+ {
2061
+ "name": "stdout",
2062
+ "output_type": "stream",
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2095
+ "\n"
2096
+ ]
2097
+ },
2098
+ {
2099
+ "name": "stderr",
2100
+ "output_type": "stream",
2101
+ "text": [
2102
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
2103
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
2104
+ ]
2105
+ },
2106
+ {
2107
+ "name": "stdout",
2108
+ "output_type": "stream",
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+ ]
2168
+ },
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+ {
2170
+ "name": "stderr",
2171
+ "output_type": "stream",
2172
+ "text": [
2173
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
2174
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
2175
+ ]
2176
+ },
2177
+ {
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+ "name": "stdout",
2179
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2239
+ ]
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+ },
2241
+ {
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+ "name": "stderr",
2243
+ "output_type": "stream",
2244
+ "text": [
2245
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
2246
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
2247
+ ]
2248
+ },
2249
+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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2273
+ ]
2274
+ },
2275
+ {
2276
+ "name": "stderr",
2277
+ "output_type": "stream",
2278
+ "text": [
2279
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
2280
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
2281
+ ]
2282
+ },
2283
+ {
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+ "output_type": "stream",
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+ ]
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+ },
2320
+ {
2321
+ "name": "stderr",
2322
+ "output_type": "stream",
2323
+ "text": [
2324
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
2325
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
2326
+ ]
2327
+ },
2328
+ {
2329
+ "name": "stdout",
2330
+ "output_type": "stream",
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+ "output_type": "stream",
2350
+ "text": [
2351
+ "Decoding error with 134,217,728 bytes, reading another chunk\n",
2352
+ "Decoding error with 134,217,728 bytes, reading another chunk\n"
2353
+ ]
2354
+ },
2355
+ {
2356
+ "name": "stdout",
2357
+ "output_type": "stream",
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2442
+ ]
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",
2455
+ "text": [
2456
+ "9.0M / 10M\n",
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2578
+ ]
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",
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+ "text": [
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2663
+ ]
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",
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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",
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+ "text": [
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2696
+ "trying to create_parquet\n",
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+ "3.0M / 10M\n"
2701
+ ]
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",
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+ "text": [
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2770
+ ]
2771
+ },
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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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+ "output_type": "stream",
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2846
+ "5.0M / 10M\n",
2847
+ "6.0M / 10M\n",
2848
+ "7.0M / 10M\n",
2849
+ "8.0M / 10M\n",
2850
+ "9.0M / 10M\n",
2851
+ "10.0M / 10M\n",
2852
+ "trying to create_parquet\n",
2853
+ "\n",
2854
+ "1.0M / 10M\n",
2855
+ "2.0M / 10M\n",
2856
+ "3.0M / 10M\n",
2857
+ "4.0M / 10M\n",
2858
+ "5.0M / 10M\n",
2859
+ "6.0M / 10M\n",
2860
+ "7.0M / 10M\n",
2861
+ "8.0M / 10M\n",
2862
+ "9.0M / 10M\n",
2863
+ "10.0M / 10M\n",
2864
+ "trying to create_parquet\n",
2865
+ "\n",
2866
+ "1.0M / 10M\n",
2867
+ "2.0M / 10M\n",
2868
+ "3.0M / 10M\n",
2869
+ "4.0M / 10M\n",
2870
+ "5.0M / 10M\n",
2871
+ "6.0M / 10M\n",
2872
+ "7.0M / 10M\n",
2873
+ "8.0M / 10M\n",
2874
+ "9.0M / 10M\n",
2875
+ "10.0M / 10M\n",
2876
+ "trying to create_parquet\n",
2877
+ "\n",
2878
+ "1.0M / 10M\n",
2879
+ "2.0M / 10M\n",
2880
+ "3.0M / 10M\n",
2881
+ "4.0M / 10M\n",
2882
+ "5.0M / 10M\n",
2883
+ "6.0M / 10M\n"
2884
+ ]
2885
+ },
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": [
2898
+ "7.0M / 10M\n",
2899
+ "8.0M / 10M\n",
2900
+ "9.0M / 10M\n",
2901
+ "10.0M / 10M\n",
2902
+ "trying to create_parquet\n",
2903
+ "\n"
2904
+ ]
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": [
2918
+ "1.0M / 10M\n",
2919
+ "2.0M / 10M\n",
2920
+ "3.0M / 10M\n",
2921
+ "4.0M / 10M\n",
2922
+ "5.0M / 10M\n"
2923
+ ]
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",
2940
+ "9.0M / 10M\n",
2941
+ "10.0M / 10M\n",
2942
+ "trying to create_parquet\n",
2943
+ "\n",
2944
+ "1.0M / 10M\n",
2945
+ "2.0M / 10M\n",
2946
+ "3.0M / 10M\n",
2947
+ "4.0M / 10M\n",
2948
+ "5.0M / 10M\n",
2949
+ "6.0M / 10M\n",
2950
+ "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",
2956
+ "1.0M / 10M\n",
2957
+ "2.0M / 10M\n",
2958
+ "3.0M / 10M\n",
2959
+ "4.0M / 10M\n",
2960
+ "5.0M / 10M\n",
2961
+ "6.0M / 10M\n",
2962
+ "7.0M / 10M\n",
2963
+ "8.0M / 10M\n",
2964
+ "9.0M / 10M\n",
2965
+ "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",
2971
+ "4.0M / 10M\n",
2972
+ "5.0M / 10M\n",
2973
+ "6.0M / 10M\n",
2974
+ "7.0M / 10M\n",
2975
+ "8.0M / 10M\n",
2976
+ "9.0M / 10M\n",
2977
+ "10.0M / 10M\n",
2978
+ "trying to create_parquet\n",
2979
+ "\n",
2980
+ "1.0M / 10M\n",
2981
+ "2.0M / 10M\n",
2982
+ "3.0M / 10M\n",
2983
+ "4.0M / 10M\n",
2984
+ "5.0M / 10M\n",
2985
+ "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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ ]
149
+ },
150
+ {
151
+ "cell_type": "code",
152
+ "execution_count": 12,
153
+ "metadata": {},
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')]"
161
+ ]
162
+ },
163
+ {
164
+ "cell_type": "code",
165
+ "execution_count": 13,
166
+ "metadata": {},
167
+ "outputs": [
168
+ {
169
+ "name": "stderr",
170
+ "output_type": "stream",
171
+ "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
+ "/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-03.zst: 1899665475 bytes \n",
175
+ "/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-04.zst: 1875866199 bytes \n",
176
+ "/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-05.zst: 1904296459 bytes \n",
177
+ "/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-06.zst: 2055584210 bytes \n",
178
+ "/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",
182
+ "/mnt/i/NLP_Datasets/Reddit/2010/RC_2010-11.zst: 3320232097 bytes \n",
183
+ "/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,
195
+ "metadata": {},
196
+ "outputs": [],
197
+ "source": []
198
+ },
199
+ {
200
+ "cell_type": "code",
201
+ "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
+ ]
211
+ },
212
+ {
213
+ "cell_type": "code",
214
+ "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
+ "/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",
230
+ "/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
+ "language": "python",
289
+ "name": "python3"
290
+ },
291
+ "language_info": {
292
+ "codemirror_mode": {
293
+ "name": "ipython",
294
+ "version": 3
295
+ },
296
+ "file_extension": ".py",
297
+ "mimetype": "text/x-python",
298
+ "name": "python",
299
+ "nbconvert_exporter": "python",
300
+ "pygments_lexer": "ipython3",
301
+ "version": "3.9.15"
302
+ },
303
+ "orig_nbformat": 4,
304
+ "vscode": {
305
+ "interpreter": {
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+ "hash": "ef741df2a7755d2d639440173889a3c1405e2c4dc3663c5e25a76822c200d193"
307
+ }
308
+ }
309
+ },
310
+ "nbformat": 4,
311
+ "nbformat_minor": 2
312
+ }