File size: 18,030 Bytes
c9f7289 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 | {
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"id": "4f90bfb1",
"metadata": {},
"outputs": [],
"source": [
"import pyarrow.parquet as pq\n",
"import pandas as pd\n",
"import random\n",
"import gc"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fedd7106",
"metadata": {},
"outputs": [],
"source": [
"# Open the Parquet file\n",
"parquet_file = pq.ParquetFile(r'C:\\Users\\marku\\Desktop\\4år\\AML\\AppliedML2025\\Final project\\antarctica ml\\AppML_2025\\tabular_train_dataset\\bedmap_train2_30m.parquet')\n",
"\n",
"# Get number of row groups\n",
"num_row_groups = parquet_file.num_row_groups\n",
"\n",
"# Select 10% of row groups randomly\n",
"sample_size = max(1, int(num_row_groups * 0.1))\n",
"selected_groups = random.sample(range(num_row_groups), sample_size)\n",
"\n",
"# Read only the selected row groups, excluding specific columns\n",
"dfs = []\n",
"for i in selected_groups:\n",
" table = parquet_file.read_row_group(i)\n",
" df = table.to_pandas()\n",
" df = df.drop(columns=['LON', 'LAT', 'geometry'], errors='ignore') # Drop unwanted columns if present\n",
" dfs.append(df)\n",
"\n",
"# Combine into one DataFrame\n",
"data = pd.concat(dfs, ignore_index=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "105c726d",
"metadata": {},
"outputs": [],
"source": [
"data.sort_values(by=['EAST', 'NORTH'], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e72f55d9",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\marku\\AppData\\Local\\Temp\\ipykernel_1488\\2804182976.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" duplicates.sort_values(by=['EAST', 'NORTH'], inplace=True)\n"
]
}
],
"source": [
"duplicates = data[data.duplicated(subset=['EAST', 'NORTH'], keep=False)]\n",
"duplicates.sort_values(by=['EAST', 'NORTH'], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "98f5dbf6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"no. duplicates: 20749.\n",
"Unique (EAST, NORTH) pairs that have duplicates: 8369\n"
]
}
],
"source": [
"print(f\"no. duplicates: {len(duplicates)}.\")\n",
"\n",
"num_duped_coord = duplicates[['EAST', 'NORTH']].drop_duplicates().shape[0]\n",
"print(\"Unique (EAST, NORTH) pairs that have duplicates:\", num_duped_coord)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7e1a206f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" EAST NORTH THICK_range THICK_mean THICK_median \\\n",
"0 -2.308745e+06 1.143082e+06 16.590 343.410000 343.915 \n",
"1 -2.141053e+06 1.032035e+06 0.000 460.420000 460.420 \n",
"2 -1.944284e+06 9.048456e+05 0.000 1290.000000 1290.000 \n",
"3 -1.907729e+06 8.049775e+05 0.000 716.000000 716.000 \n",
"4 -1.902475e+06 9.188595e+05 5.060 913.010000 913.010 \n",
"5 -1.900838e+06 8.852626e+05 0.000 407.000000 407.000 \n",
"6 -1.875244e+06 8.974816e+05 0.000 1260.000000 1260.000 \n",
"7 -1.800857e+06 8.888363e+05 0.000 770.000000 770.000 \n",
"8 -1.766391e+06 7.534504e+05 0.000 763.000000 763.000 \n",
"9 -1.708213e+06 8.119492e+05 0.000 1267.000000 1267.000 \n",
"10 -1.698118e+06 7.934723e+05 0.000 612.000000 612.000 \n",
"11 -1.695179e+06 6.702288e+05 7.928 762.671000 762.671 \n",
"12 -1.689698e+06 -1.828034e+05 4.350 826.545000 826.545 \n",
"13 -1.688817e+06 -1.826473e+05 1.740 858.260000 858.260 \n",
"14 -1.677457e+06 -1.213272e+05 0.350 1819.685000 1819.685 \n",
"15 -1.672095e+06 7.657638e+05 0.000 249.000000 249.000 \n",
"16 -1.641989e+06 5.976326e+05 2.990 1706.393333 1706.630 \n",
"17 -1.628775e+06 4.224858e+05 0.000 1300.000000 1300.000 \n",
"18 -1.627830e+06 4.993682e+05 0.000 943.000000 943.000 \n",
"19 -1.622924e+06 -2.434122e+05 4.600 502.560000 502.560 \n",
"20 -1.622312e+06 -2.459353e+05 0.220 690.100000 690.100 \n",
"21 -1.622251e+06 -2.462038e+05 1.570 704.635000 704.635 \n",
"22 -1.621120e+06 -2.512063e+05 4.950 710.475000 710.475 \n",
"23 -1.620706e+06 -2.522192e+05 3.240 716.200000 716.200 \n",
"24 -1.618462e+06 -2.385436e+05 1.170 663.165000 663.165 \n",
"\n",
" THICK_range_ratio \n",
"0 0.048310 \n",
"1 0.000000 \n",
"2 0.000000 \n",
"3 0.000000 \n",
"4 0.005542 \n",
"5 0.000000 \n",
"6 0.000000 \n",
"7 0.000000 \n",
"8 0.000000 \n",
"9 0.000000 \n",
"10 0.000000 \n",
"11 0.010395 \n",
"12 0.005263 \n",
"13 0.002027 \n",
"14 0.000192 \n",
"15 0.000000 \n",
"16 0.001752 \n",
"17 0.000000 \n",
"18 0.000000 \n",
"19 0.009153 \n",
"20 0.000319 \n",
"21 0.002228 \n",
"22 0.006967 \n",
"23 0.004524 \n",
"24 0.001764 \n"
]
}
],
"source": [
"summary = (\n",
" duplicates.groupby(['EAST', 'NORTH']).agg(\n",
" THICK_range=('THICK', lambda x: x.max() - x.min()),\n",
" THICK_mean=('THICK', 'mean'),\n",
" THICK_median=('THICK', 'median')\n",
" ).reset_index()\n",
")\n",
"summary['THICK_range_ratio'] = summary['THICK_range'] / summary['THICK_mean']\n",
"\n",
"print(summary.head(25))\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2b7c1579",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0030684829606210935\n",
"0.017637902014526745\n",
"8369\n",
"2317\n",
"1259\n"
]
}
],
"source": [
"print(summary['THICK_range_ratio'].median())\n",
"print(summary['THICK_range_ratio'].mean())\n",
"print(len(summary))\n",
"print(len(summary[summary['THICK_range_ratio'] > 0.01]))\n",
"print(len(summary[(summary['THICK_range_ratio'] > 0.025)]))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d4ea1f81",
"metadata": {},
"outputs": [],
"source": [
"#Keep only rows with THICK_range_ratio <= 0.025\n",
"summary = summary[summary['THICK_range_ratio'] <= 0.025]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "5b63659d",
"metadata": {},
"outputs": [],
"source": [
"# Step 1: Merge df with summary to bring in the Thick_median\n",
"merged = data.merge(summary, on=['EAST', 'NORTH'], how='left')\n",
"\n",
"# Step 2: Keep either:\n",
"# - rows not in summary (i.e., Thick_median is NaN)\n",
"# - or rows where Thick == Thick_median\n",
"result = merged[\n",
" merged['THICK_median'].isna() |\n",
" (merged['THICK'] == merged['THICK_median'])\n",
"]\n",
"del merged\n",
"gc.collect()\n",
"\n",
"\n",
"\n",
"# Optional: Drop Thick_median column if not needed\n",
"result = result.drop(columns=['THICK_median', 'THICK_range', 'THICK_mean', 'THICK_range_ratio'], errors='ignore')\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "c45e6e83",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>THICK</th>\n",
" <th>EAST</th>\n",
" <th>NORTH</th>\n",
" <th>vx</th>\n",
" <th>vy</th>\n",
" <th>v</th>\n",
" <th>ith_bm</th>\n",
" <th>smb</th>\n",
" <th>z</th>\n",
" <th>s</th>\n",
" <th>temp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>50.82</td>\n",
" <td>-2.498779e+06</td>\n",
" <td>1.417597e+06</td>\n",
" <td>-1133.923586</td>\n",
" <td>538.882191</td>\n",
" <td>1255.458766</td>\n",
" <td>85.334967</td>\n",
" <td>1607.108764</td>\n",
" <td>278.650876</td>\n",
" <td>0.049565</td>\n",
" <td>266.860876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>53.63</td>\n",
" <td>-2.498462e+06</td>\n",
" <td>1.417028e+06</td>\n",
" <td>-1104.194542</td>\n",
" <td>518.784315</td>\n",
" <td>1219.992931</td>\n",
" <td>69.791700</td>\n",
" <td>1669.475359</td>\n",
" <td>305.152998</td>\n",
" <td>0.055753</td>\n",
" <td>266.809909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>23.24</td>\n",
" <td>-2.497579e+06</td>\n",
" <td>1.415438e+06</td>\n",
" <td>-797.720551</td>\n",
" <td>252.445367</td>\n",
" <td>836.711863</td>\n",
" <td>44.747269</td>\n",
" <td>1834.636673</td>\n",
" <td>365.113957</td>\n",
" <td>0.020203</td>\n",
" <td>266.596713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>21.80</td>\n",
" <td>-2.495786e+06</td>\n",
" <td>1.412343e+06</td>\n",
" <td>-65.286477</td>\n",
" <td>146.920962</td>\n",
" <td>160.773421</td>\n",
" <td>19.385211</td>\n",
" <td>2213.076648</td>\n",
" <td>499.961841</td>\n",
" <td>0.046409</td>\n",
" <td>265.990232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>25.30</td>\n",
" <td>-2.495772e+06</td>\n",
" <td>1.412320e+06</td>\n",
" <td>-63.979106</td>\n",
" <td>147.167889</td>\n",
" <td>160.473404</td>\n",
" <td>19.363090</td>\n",
" <td>2215.949898</td>\n",
" <td>501.047693</td>\n",
" <td>0.045488</td>\n",
" <td>265.986040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2097147</th>\n",
" <td>2000.64</td>\n",
" <td>2.654522e+06</td>\n",
" <td>-4.884883e+05</td>\n",
" <td>0.449748</td>\n",
" <td>15.509423</td>\n",
" <td>15.515943</td>\n",
" <td>147.532692</td>\n",
" <td>765.603721</td>\n",
" <td>182.417924</td>\n",
" <td>0.040650</td>\n",
" <td>261.717844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2097148</th>\n",
" <td>1955.43</td>\n",
" <td>2.654789e+06</td>\n",
" <td>-4.886187e+05</td>\n",
" <td>0.618483</td>\n",
" <td>15.115018</td>\n",
" <td>15.127666</td>\n",
" <td>155.331007</td>\n",
" <td>763.821258</td>\n",
" <td>181.186072</td>\n",
" <td>0.040658</td>\n",
" <td>261.736247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2097149</th>\n",
" <td>1973.15</td>\n",
" <td>2.654843e+06</td>\n",
" <td>-4.886439e+05</td>\n",
" <td>0.630447</td>\n",
" <td>15.065385</td>\n",
" <td>15.078570</td>\n",
" <td>156.902040</td>\n",
" <td>763.393967</td>\n",
" <td>180.894478</td>\n",
" <td>0.040675</td>\n",
" <td>261.740045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2097150</th>\n",
" <td>1905.27</td>\n",
" <td>2.655007e+06</td>\n",
" <td>-4.887185e+05</td>\n",
" <td>0.433723</td>\n",
" <td>14.901482</td>\n",
" <td>14.907793</td>\n",
" <td>161.674849</td>\n",
" <td>762.025022</td>\n",
" <td>179.962857</td>\n",
" <td>0.040781</td>\n",
" <td>261.751564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2097151</th>\n",
" <td>1893.28</td>\n",
" <td>2.655062e+06</td>\n",
" <td>-4.887430e+05</td>\n",
" <td>0.326494</td>\n",
" <td>14.848336</td>\n",
" <td>14.851925</td>\n",
" <td>163.469380</td>\n",
" <td>761.494619</td>\n",
" <td>179.626606</td>\n",
" <td>0.040890</td>\n",
" <td>261.755432</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2083679 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" THICK EAST NORTH vx vy \\\n",
"0 50.82 -2.498779e+06 1.417597e+06 -1133.923586 538.882191 \n",
"1 53.63 -2.498462e+06 1.417028e+06 -1104.194542 518.784315 \n",
"2 23.24 -2.497579e+06 1.415438e+06 -797.720551 252.445367 \n",
"3 21.80 -2.495786e+06 1.412343e+06 -65.286477 146.920962 \n",
"4 25.30 -2.495772e+06 1.412320e+06 -63.979106 147.167889 \n",
"... ... ... ... ... ... \n",
"2097147 2000.64 2.654522e+06 -4.884883e+05 0.449748 15.509423 \n",
"2097148 1955.43 2.654789e+06 -4.886187e+05 0.618483 15.115018 \n",
"2097149 1973.15 2.654843e+06 -4.886439e+05 0.630447 15.065385 \n",
"2097150 1905.27 2.655007e+06 -4.887185e+05 0.433723 14.901482 \n",
"2097151 1893.28 2.655062e+06 -4.887430e+05 0.326494 14.848336 \n",
"\n",
" v ith_bm smb z s \\\n",
"0 1255.458766 85.334967 1607.108764 278.650876 0.049565 \n",
"1 1219.992931 69.791700 1669.475359 305.152998 0.055753 \n",
"2 836.711863 44.747269 1834.636673 365.113957 0.020203 \n",
"3 160.773421 19.385211 2213.076648 499.961841 0.046409 \n",
"4 160.473404 19.363090 2215.949898 501.047693 0.045488 \n",
"... ... ... ... ... ... \n",
"2097147 15.515943 147.532692 765.603721 182.417924 0.040650 \n",
"2097148 15.127666 155.331007 763.821258 181.186072 0.040658 \n",
"2097149 15.078570 156.902040 763.393967 180.894478 0.040675 \n",
"2097150 14.907793 161.674849 762.025022 179.962857 0.040781 \n",
"2097151 14.851925 163.469380 761.494619 179.626606 0.040890 \n",
"\n",
" temp \n",
"0 266.860876 \n",
"1 266.809909 \n",
"2 266.596713 \n",
"3 265.990232 \n",
"4 265.986040 \n",
"... ... \n",
"2097147 261.717844 \n",
"2097148 261.736247 \n",
"2097149 261.740045 \n",
"2097150 261.751564 \n",
"2097151 261.755432 \n",
"\n",
"[2083679 rows x 11 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "appml25",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|