0. Exploración y preparación de datos.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.2"
    },
    "colab": {
      "name": "0. Exploración y preparación de datos.ipynb",
      "provenance": [],
      "toc_visible": true
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "PsYQIouKaPWv"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-IaNQ33aPW0",
        "outputId": "688fb053-c334-474f-edb5-e444d179b17f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "X_train=pd.read_csv(\"https://raw.githubusercontent.com/AnIsAsPe/DeteccionDeAnomalias/master/Datos/train_X_9jXqqNo.csv\")\n",
        "X_train.head(3)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Unnamed: 0</th>\n",
              "      <th>SAMPLE_ID</th>\n",
              "      <th>GROUP_ID</th>\n",
              "      <th>ASSET_ID</th>\n",
              "      <th>MEASURE_TYPE</th>\n",
              "      <th>MEASURE_VALUE</th>\n",
              "      <th>MEASURE_WEEKDAY</th>\n",
              "      <th>MEASURE_WEEK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  SAMPLE_ID  ...  MEASURE_WEEKDAY  MEASURE_WEEK\n",
              "0           0          1  ...                1             1\n",
              "1           1          1  ...                1             1\n",
              "2           2          1  ...                1             1\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "DNslGZkeaPW4",
        "outputId": "eb6dfe15-5734-45c9-c971-e8104677aa22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "X_train = X_train.drop('Unnamed: 0', axis=1)\n",
        "X_train.columns= map(str.lower, X_train.columns)\n",
        "print(X_train.shape)\n",
        "X_train.head(3)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(241696, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>sample_id</th>\n",
              "      <th>group_id</th>\n",
              "      <th>asset_id</th>\n",
              "      <th>measure_type</th>\n",
              "      <th>measure_value</th>\n",
              "      <th>measure_weekday</th>\n",
              "      <th>measure_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sample_id  group_id  asset_id  ...  measure_value  measure_weekday  measure_week\n",
              "0          1         2         1  ...            NaN                1             1\n",
              "1          1         2         1  ...            NaN                1             1\n",
              "2          1         2         1  ...           19.0                1             1\n",
              "\n",
              "[3 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaOqFsyBaPW7",
        "outputId": "fd799e33-25d9-4a0f-aeea-d3682c0a5c3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "X_train.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sample_id</th>\n",
              "      <th>group_id</th>\n",
              "      <th>asset_id</th>\n",
              "      <th>measure_type</th>\n",
              "      <th>measure_value</th>\n",
              "      <th>measure_weekday</th>\n",
              "      <th>measure_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>241696.000000</td>\n",
              "      <td>241696.000000</td>\n",
              "      <td>241696.000000</td>\n",
              "      <td>241696.000000</td>\n",
              "      <td>170352.000000</td>\n",
              "      <td>241696.000000</td>\n",
              "      <td>241696.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>52.500000</td>\n",
              "      <td>2.674699</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>19.270992</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>52.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>30.020888</td>\n",
              "      <td>0.468488</td>\n",
              "      <td>23.958347</td>\n",
              "      <td>1.118036</td>\n",
              "      <td>45.299878</td>\n",
              "      <td>2.000004</td>\n",
              "      <td>30.020888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>26.750000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>26.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>52.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>52.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>78.250000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>3.250000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>78.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>104.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>728.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>104.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           sample_id       group_id  ...  measure_weekday   measure_week\n",
              "count  241696.000000  241696.000000  ...    241696.000000  241696.000000\n",
              "mean       52.500000       2.674699  ...         4.000000      52.500000\n",
              "std        30.020888       0.468488  ...         2.000004      30.020888\n",
              "min         1.000000       2.000000  ...         1.000000       1.000000\n",
              "25%        26.750000       2.000000  ...         2.000000      26.750000\n",
              "50%        52.500000       3.000000  ...         4.000000      52.500000\n",
              "75%        78.250000       3.000000  ...         6.000000      78.250000\n",
              "max       104.000000       3.000000  ...         7.000000     104.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FdIEjyxaPW9",
        "outputId": "8c7388cb-e477-41dc-bbc2-4c01b5004d1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "source": [
        "#Se tiene la hipotesis Sample ID == MESSURE _WEEK\n",
        "def correlacion(dataframe, grafica = True, figsize=(8,6)):\n",
        "    corr=dataframe.corr()                #creamos la matriz de correlación\n",
        "    fig = plt.figure(figsize = figsize)\n",
        "    if grafica == True:\n",
        "        sns.heatmap(corr, cmap=\"coolwarm\", annot=True, square=False)\n",
        "        fig.tight_layout()\n",
        "    return corr\n",
        "\n",
        "corr_matrix=correlacion(X_train, grafica=True)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HObpOsWBaPXA",
        "outputId": "8d641fb3-99e4-4f4c-d4ee-15135266f3b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "X_train = X_train.drop('sample_id', axis=1)\n",
        "\n",
        "for col in X_train.columns:\n",
        "    print(col)\n",
        "    print(X_train[col].unique(),\"\\n\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "group_id\n",
            "[2 3] \n",
            "\n",
            "asset_id\n",
            "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
            " 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48\n",
            " 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72\n",
            " 73 74 75 76 77 78 79 80 81 82 83] \n",
            "\n",
            "measure_type\n",
            "[1 2 3 4] \n",
            "\n",
            "measure_value\n",
            "[ nan  19.   0.   8.   9.   6.   2.  33.   3.  31.  23.  14.   7.  58.\n",
            "  10.  35.  51.  38.  48.  11.  56.   5.  50.  36.  27.  22.   1.  21.\n",
            "   4.  28.  12.  13.  15.  26.  17.  29.  16.  20.  24.  18.  41.  30.\n",
            "  32.  34.  42.  43.  47.  25.  60.  59.  44.  40.  45.  55.  94.  70.\n",
            "  57.  64.  68.  53.  49.  52.  63.  54.  66.  37.  46.  89.  77.  90.\n",
            "  61.  39.  83.  97. 104.  69.  71.  65.  82.  74.  84.  81.  75.  88.\n",
            " 136. 143. 150. 158. 165. 177.  78. 196. 100. 206. 116. 237. 138. 260.\n",
            " 137. 253. 229. 135. 273. 178. 242. 141. 246. 134. 282. 164. 294. 191.\n",
            " 297. 303. 181. 289. 270. 255. 155. 276. 154. 277. 170. 304. 197. 316.\n",
            " 208. 264. 171. 261. 160. 225. 139. 256. 111. 190.  98. 124.  62. 127.\n",
            "  67.  72.  87.  93.  91.  96. 103.  86.  92. 142. 149. 176. 146. 112.\n",
            " 110. 119. 117. 132. 113. 105.  99. 114. 115.  76.  73. 140. 184. 179.\n",
            " 199. 180. 214.  95. 166. 163. 167. 169. 161. 121.  85.  79. 131. 102.\n",
            " 147. 125. 156. 186. 173. 215. 244. 236. 130. 228. 233. 247. 128. 205.\n",
            " 183. 232. 281. 153. 259. 230. 238. 122. 243. 109. 157. 218. 108. 159.\n",
            " 174. 133. 151. 168. 107. 152. 144. 148.  80. 224. 266. 300. 274. 262.\n",
            " 280. 298. 101. 202. 193. 123. 120. 106. 216. 227. 263. 275. 331. 295.\n",
            " 364. 342. 361. 313. 335. 319. 312. 322. 346. 118. 324. 279. 269. 252.\n",
            " 194. 145. 212. 220. 198. 207. 192. 129. 271. 317. 328. 240. 311. 223.\n",
            " 315. 250. 362. 347. 254. 354. 239. 325. 231. 213. 310. 307. 235. 296.\n",
            " 204. 278. 337. 327. 306. 221. 348. 338. 217. 359. 201. 329. 285. 182.\n",
            " 195. 126. 172. 211. 292. 283. 305. 378. 162. 425. 385. 435. 428. 439.\n",
            " 424. 418. 412. 392. 409. 267. 437. 421. 411. 249. 395. 257. 302. 272.\n",
            " 367. 351. 309. 352. 377. 370. 175. 360. 320. 339. 318. 286. 187. 188.\n",
            " 268. 308. 210. 185. 265. 284. 203. 326. 219. 200. 355. 345. 340. 291.\n",
            " 226. 251. 323. 369. 402. 371. 405. 383. 413. 434. 393. 384. 375. 376.\n",
            " 349. 372. 433. 452. 500. 549. 548. 497. 547. 513. 258. 516. 504. 507.\n",
            " 532. 537. 470. 448. 508. 501. 458. 390. 391. 415. 314. 343. 234. 293.\n",
            " 209. 248. 379. 365. 373. 430. 330. 368. 332. 222. 189. 382. 363. 419.\n",
            " 403. 422. 468. 429. 394. 408. 386. 387. 404. 388. 410. 451. 486. 462.\n",
            " 478. 469. 464. 491. 476. 416. 423. 436. 441. 396. 288. 357. 489. 449.\n",
            " 503. 523. 494. 417. 496. 498. 527. 490. 467. 479. 454. 241. 321. 341.\n",
            " 356. 334. 301. 336. 299. 245. 397. 389. 398. 400. 290. 287. 401. 525.\n",
            " 531. 445. 511. 619. 647. 666. 717. 601. 639. 648. 661. 675. 695. 724.\n",
            " 697. 517. 606. 676. 584. 652. 596. 635. 636. 658. 727. 631. 728. 665.\n",
            " 551. 590. 605. 578. 645. 594. 539. 420. 485. 350. 536. 610. 564. 630.\n",
            " 643. 704. 703. 484. 632. 593. 554. 598. 609. 540. 344. 674. 680. 568.\n",
            " 576. 563. 519. 538. 447. 427. 459. 333. 471. 466. 432. 457. 556. 510.\n",
            " 358. 366. 381. 481. 544. 465. 488. 453. 443. 426. 461. 446. 495. 374.\n",
            " 399. 460. 353. 440. 407. 414. 558. 522. 499. 444. 526. 472. 442. 380.\n",
            " 406. 438. 506. 477. 582. 493. 463. 487. 474.] \n",
            "\n",
            "measure_weekday\n",
            "[1 2 3 4 5 6 7] \n",
            "\n",
            "measure_week\n",
            "[  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18\n",
            "  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36\n",
            "  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54\n",
            "  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72\n",
            "  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90\n",
            "  91  92  93  94  95  96  97  98  99 100 101 102 103 104] \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAumasVLaPXC",
        "outputId": "f6588ae6-b2a5-4f80-8f9d-2da21fe97302",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "sns.set(style=\"ticks\", palette=\"muted\")\n",
        "\n",
        "\n",
        "sns.boxplot(x=\"measure_type\", y=\"measure_value\",\n",
        "            hue=\"group_id\", palette=[\"m\",\"g\"], \n",
        "            data=X_train)\n",
        "sns.despine(offset=10, trim=True)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldLiZp_4aPXE",
        "outputId": "6c86fa82-0cae-4256-e4f2-8b5221671968",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "X_train.head(3)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>group_id</th>\n",
              "      <th>asset_id</th>\n",
              "      <th>measure_type</th>\n",
              "      <th>measure_value</th>\n",
              "      <th>measure_weekday</th>\n",
              "      <th>measure_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   group_id  asset_id  ...  measure_weekday  measure_week\n",
              "0         2         1  ...                1             1\n",
              "1         2         1  ...                1             1\n",
              "2         2         1  ...                1             1\n",
              "\n",
              "[3 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ENyHz6BwaPXG"
      },
      "source": [
        "## Formateo de DataFrame\n",
        "\n",
        "De manera que nos quedemos con un renglon con las cuatro mediciones para cada día en cada instalación"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWahr0GRaPXH",
        "outputId": "1dd98b02-651b-4bda-e24d-58e93f41c2b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "X_train=pd.pivot_table(X_train, index=['asset_id', 'group_id','measure_week', 'measure_weekday'], \n",
        "               columns=['measure_type'],values=['measure_value'],aggfunc='mean',).reset_index()\n",
        "X_train.columns=['asset_id', 'group_id','week', 'weekday', 't1','t2','t3','t4']\n",
        "X_train.head(3)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>asset_id</th>\n",
              "      <th>group_id</th>\n",
              "      <th>week</th>\n",
              "      <th>weekday</th>\n",
              "      <th>t1</th>\n",
              "      <th>t2</th>\n",
              "      <th>t3</th>\n",
              "      <th>t4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   asset_id  group_id  week  weekday  t1  t2    t3   t4\n",
              "0         1         2     1        1 NaN NaN  19.0  0.0\n",
              "1         1         2     1        2 NaN NaN   8.0  0.0\n",
              "2         1         2     1        3 NaN NaN   9.0  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7D5TUgTaPXJ",
        "outputId": "09d230e2-6bc6-4b5d-dee8-487b9da0e003",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "X_train.describe()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>asset_id</th>\n",
              "      <th>group_id</th>\n",
              "      <th>week</th>\n",
              "      <th>weekday</th>\n",
              "      <th>t1</th>\n",
              "      <th>t2</th>\n",
              "      <th>t3</th>\n",
              "      <th>t4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>60424.000000</td>\n",
              "      <td>60424.000000</td>\n",
              "      <td>60424.000000</td>\n",
              "      <td>60424.000000</td>\n",
              "      <td>24752.000000</td>\n",
              "      <td>24752.000000</td>\n",
              "      <td>60424.000000</td>\n",
              "      <td>60424.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>2.674699</td>\n",
              "      <td>52.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.536765</td>\n",
              "      <td>0.349548</td>\n",
              "      <td>45.185075</td>\n",
              "      <td>8.372484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>23.958495</td>\n",
              "      <td>0.468491</td>\n",
              "      <td>30.021075</td>\n",
              "      <td>2.000017</td>\n",
              "      <td>5.029422</td>\n",
              "      <td>2.004578</td>\n",
              "      <td>64.641355</td>\n",
              "      <td>23.002216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>26.750000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>52.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>63.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>78.250000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>83.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>728.000000</td>\n",
              "      <td>299.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           asset_id      group_id  ...            t3            t4\n",
              "count  60424.000000  60424.000000  ...  60424.000000  60424.000000\n",
              "mean      42.000000      2.674699  ...     45.185075      8.372484\n",
              "std       23.958495      0.468491  ...     64.641355     23.002216\n",
              "min        1.000000      2.000000  ...      0.000000      0.000000\n",
              "25%       21.000000      2.000000  ...      9.000000      0.000000\n",
              "50%       42.000000      3.000000  ...     25.000000      1.000000\n",
              "75%       63.000000      3.000000  ...     54.000000      6.000000\n",
              "max       83.000000      3.000000  ...    728.000000    299.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTMFMThwaPXL",
        "outputId": "9d024a8c-ae95-41ce-bb6b-d6b607c96940",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "X_train.info()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 60424 entries, 0 to 60423\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   asset_id  60424 non-null  int64  \n",
            " 1   group_id  60424 non-null  int64  \n",
            " 2   week      60424 non-null  int64  \n",
            " 3   weekday   60424 non-null  int64  \n",
            " 4   t1        24752 non-null  float64\n",
            " 5   t2        24752 non-null  float64\n",
            " 6   t3        60424 non-null  float64\n",
            " 7   t4        60424 non-null  float64\n",
            "dtypes: float64(4), int64(4)\n",
            "memory usage: 3.7 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9XpLS5XaPXN",
        "outputId": "66696ec3-d598-465b-dd52-8daf552bcbb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "sns.set(style=\"ticks\", palette=\"muted\")\n",
        "\n",
        "\n",
        "sns.boxplot(x=\"weekday\", y=\"t3\",\n",
        "            hue=\"group_id\", palette=[\"m\",\"g\"], \n",
        "            data=X_train)\n",
        "sns.despine(offset=10, trim=True)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PfJELSAnaPXO",
        "outputId": "fe8f62e3-fc56-4fd8-8aa5-861d6d030e5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "plt.hist(X_train[X_train['group_id']==3]['t3'], bins=20, alpha=.5, label=\"grupo 3\")\n",
        "plt.hist(X_train[X_train['group_id']==2]['t3'], bins=20, alpha=.5, label=\"grupo 2\")\n",
        "plt.legend()\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6d0d366fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Npviiep-aPXQ",
        "outputId": "f978eb2e-ad96-4d05-d24d-792b80eeb292",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "mediciones=[\"t1\",\"t2\",\"t3\",\"t4\"]\n",
        "X_train[mediciones].plot(kind='hist', stacked=True, bins=50, alpha=.5)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d0d36c1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uC0E7lOVaPXT",
        "outputId": "5da73119-dd22-4b61-901a-613d6d18ad1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "X_train.t3.plot.line()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d09a84be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXUXEgZ3aPXV",
        "outputId": "f3d0cc79-420f-4e8a-eb35-160183c18244",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "corr_types=correlacion(X_train[mediciones], grafica=True,figsize=(4,3))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4lEdWFCaPXW"
      },
      "source": [
        "\n",
        "\n",
        "![image.png](attachment:image.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bC2IYQGzaPXX"
      },
      "source": [
        "# Exploración de valores nulos en el DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jju-VhJAaPXX",
        "outputId": "5095b137-bd14-4a77-85fa-4fb64eb36a2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "X_train.isnull().sum()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "asset_id        0\n",
              "group_id        0\n",
              "week            0\n",
              "weekday         0\n",
              "t1          35672\n",
              "t2          35672\n",
              "t3              0\n",
              "t4              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRR1lNUBaPXZ",
        "outputId": "3fdb8c52-97da-471d-8c19-5f91604403f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "df_nulos=X_train[X_train.t1.isnull()]\n",
        "df_nulos.describe()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>asset_id</th>\n",
              "      <th>group_id</th>\n",
              "      <th>week</th>\n",
              "      <th>weekday</th>\n",
              "      <th>t1</th>\n",
              "      <th>t2</th>\n",
              "      <th>t3</th>\n",
              "      <th>t4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>35672.000000</td>\n",
              "      <td>35672.000000</td>\n",
              "      <td>35672.000000</td>\n",
              "      <td>35672.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35672.000000</td>\n",
              "      <td>35672.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36.877551</td>\n",
              "      <td>2.551020</td>\n",
              "      <td>52.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.440401</td>\n",
              "      <td>6.569634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>23.942982</td>\n",
              "      <td>0.497397</td>\n",
              "      <td>30.021247</td>\n",
              "      <td>2.000028</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>49.357624</td>\n",
              "      <td>20.114538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>26.750000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>52.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>78.250000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>82.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>283.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           asset_id      group_id  ...            t3            t4\n",
              "count  35672.000000  35672.000000  ...  35672.000000  35672.000000\n",
              "mean      36.877551      2.551020  ...     35.440401      6.569634\n",
              "std       23.942982      0.497397  ...     49.357624     20.114538\n",
              "min        1.000000      2.000000  ...      0.000000      0.000000\n",
              "25%       17.000000      2.000000  ...      8.000000      0.000000\n",
              "50%       32.000000      3.000000  ...     22.000000      1.000000\n",
              "75%       55.000000      3.000000  ...     44.000000      5.000000\n",
              "max       82.000000      3.000000  ...    549.000000    283.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgByTfPBaPXb",
        "outputId": "6a73afbb-a41c-4bb4-e23d-0f6781a47faa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "\n",
        "for col in df_nulos.columns:\n",
        "    print(col)\n",
        "    print(df_nulos[col].nunique(),\"\\n\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "asset_id\n",
            "49 \n",
            "\n",
            "group_id\n",
            "2 \n",
            "\n",
            "week\n",
            "104 \n",
            "\n",
            "weekday\n",
            "7 \n",
            "\n",
            "t1\n",
            "0 \n",
            "\n",
            "t2\n",
            "0 \n",
            "\n",
            "t3\n",
            "439 \n",
            "\n",
            "t4\n",
            "235 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYOgV3ipaPXd",
        "outputId": "94607e6f-bbda-49a2-c88b-1c45ecd72c3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "df_nulos.group_id.value_counts()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    19656\n",
              "2    16016\n",
              "Name: group_id, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gznqGOKhaPXg",
        "outputId": "6dc6cd5c-d7b0-449f-edec-44aa49683a4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "corr_types=correlacion(df_nulos[['t3','t4']], grafica=True,figsize=(4,3))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAARQAAADMCAYAAACyalyPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAaUUlEQVR4nO3df1RUdd4H8Pe9AwqCI5Kgg+j6G02wrFbX0l0Vf5BPBNZjumKamq4S1lq7Au3mr8rSTfesP9JszUjzyYM/ckE0pOcpw1x7Ko/4KP6GQ/wSBWkCBplfzx8cJ8dp7gzy9Q4M79c595zhfr9z+d45hw+f+713vh/JarVaQUQkgOzpARCR92BAISJhGFCISBgGFCIShgGFiIRhQCEiYRhQiFqp1atXY+zYsYiIiMCFCxd+sY/ZbMaKFSswbtw4jB8/Hunp6c1uU+LTvFMiIk+Jjo7GzJkzkZCQ4LRPRkYGioqKkJ2djerqasTHx2PEiBEIDw+/6zYlzFCIWqlHHnkEOp1OsU9WVhamTJkCWZYRHByMcePG4fDhw81qU8IMhagF0ev10Ov1Dvu1Wi20Wm2Tj1dWVoawsDDbzzqdDuXl5c1qU6JqQDnoG6Hmr2uz3orZ6ukhtBm5Gb9rUn9XfwNX1iVh48aNDvuTkpKwaNGiJv0uT2CGQqQiyVdSbJ81axYmT57ssP9ushOgMbMoLS3FkCFDANhnHnfbpoRzKEQq0vhrFDetVovw8HCH7W4DSkxMDNLT02GxWFBVVYWcnBxMnDixWW1KmKEQqUjjL+5/+BtvvIHs7Gxcv34ds2fPRlBQEA4ePIh58+bhxRdfRFRUFOLi4nDq1ClMmDABAPDCCy+gR48eAHDXbUokNZcv4ByKOjiHop6mzqH8d68hiu1jC/OaMxyPY4ZCpCKRGUpLxIBCpCJJozwp29oxoBCpSOPLDIWIBJE0DChEJAgzFCISRuYcChGJIvtqPD2Ee4oBhUhFkswMhYgE4RwKEQkjyQwoRCQIMxQiEkb24aQsEQnCSVkiEkb24SUPEQnCSVkiEoaTskQkjMgMpaCgACkpKaiurkZQUBBWr16NXr162fW5du0ali5diuLiYphMJixYsABxcXEAgCVLluD8+fO2vufPn8emTZsQHR2NDRs2YNeuXQgNDQUAPPTQQ1i2bJnLMTGgEKlI5BzKsmXLMH36dMTFxeHAgQNYunQpPvroI7s+b7/9NiIjI7F582ZUVVXhqaeewrBhw6DT6bBmzRpbv3PnzmHWrFkYNWqUbV98fDySk5ObNCbvzr+IWhhJlhU3vV6P4uJih+3OWj2VlZU4e/YsnnjiCQDAE088gbNnz6Kqqsqu37lz52xBIjg4GAMHDsShQ4ccxrVnzx7ExsaiXbt2zTo/ZihEKnL1HEpaWppbdXnKysrQtWtXaDSNx9NoNAgNDUVZWRmCg4Nt/QYPHoysrCxERUWhuLgYJ0+edCgn2tDQgIyMDHz44Yd2+w8ePIjc3FyEhIRg0aJFGDp0qMvzY0AhUpOkbl2elJQUrFq1CnFxcQgLC8OIESNsQeiWnJwchIWFYdCgQbZ906ZNw4IFC+Dr64tjx44hMTERWVlZ6Ny5s+LvY0AhUpGrDMXdkqM6nQ5Xr16F2WyGRqOB2WxGRUWFQ63j4OBgvPPOO7af582bh379+tn12bt3L55++mm7fSEhIbbXjz32GHQ6HS5evIhhw4YpjotzKEQqkmRJcXPXfffdh0GDBiEzMxMAkJmZiUGDBtld7gDAjRs3YDKZAADHjx/HhQsXbPMuAFBeXo7vvvsOsbGxdu+7evWq7XV+fj5KSkrQu3dvl+NihkKkIpHf5Vm+fDlSUlLw7rvvQqvVYvXq1QBgV+grLy8Pb775JmRZRufOnbFlyxb4+/vbjrF//36MGTMGnTp1sjv2unXrcObMGciyDF9fX6xZs8Yua3GGhb68EAt9qaephb4q/vKcYnvomx/e9VhaAmYoRCqSXEzKtnYMKEQq4vIFRCQOvxxIRKLIGmYoRCQKF1giIlE4h9JG/SoxAeEzn0LHyAEo3Z2JvLmpnh5Sq9Mx0AepL0bg10M740e9Ee99VIAjX1Y49PP1kfDS/H747W+6wMdHwul8Pf626QKuVzUAAF57eSAefiAI/n4aVN1owMf7fkBmdrnapyOGxDmUNulmaQUurXoXIRNGQfZv7+nhtEqvLOgPo8mCJ5/9Gv37BGLN0ihcKqhBQVGdXb8pT4YjcqAWs178FrW1JixJGoDFf+iHv7x1FgCwc08R3l5/HkaTFT3D/bFh1YO4eLkG5y/XeOK0mkXy8gzFu8NlM5R/egRX//U5GiqrPT2UVsmvvYzfPdoF/9xZCEO9BXln9cj9phITx3R16Kvr6ocT39/AjWojGoxWfP7VNfTuGWBrLyiqg9HU+Pyl1QpYrVZ01/k7HKc1kCRJcWvtmKHQPdGjeweYLVb8UGqw7btcUIMHI4Mc+mYeKcNL8/rhvuB2qKk1YcLoUPz7O/t1PV5Z2A+PR3eDX3sNzl/+Cce/rbzn53AveHuGohhQLly4AEmS0L9/fxQWFuKLL77AgAED8OijjyoeVK/XOywIQ22Lv58GtXVmu301tSZ08Hf8gyouNaDi+k0cSBsBk9mKK4U1WLclz67P2s2X8Pf3LiFyoBZDI4PQYFTtGyNitdU5lB07dmD79u0wmUyYO3cuDhw4gKioKHzyySd49tlnkZCQ4PSgzhaJWSdmzNQKGOrNCOhgHzwCOvigzmB26Pvygv5o5yvj8d8fQ329GdOf7oG1y6Mw/08n7fpZLEDeWT0mjO6KyZPCsCej5J6ew70gtdXnUNLT05GZmYm6ujpER0fjs88+Q7du3VBVVYU5c+YoBhRni8Sc6h0tZtTU4v1QUgeNLCFc54/issbLnn69A1BQVOvQt3+fQGzdUYCfahq/Zr83swTzZvRGJ60PftSbHPprNBK6d/O7tydwr3j5cyhO8y9ZltGhQwd06dIFPXr0QLdu3QA0LtjiavJIq9UiPDzcYWtNJI0Gcvt2kDTyba+9+7+LSPU3Lfjy+HU8n9ALfu1lRA3SYuTwLvjsf6469M2/+BNixnZFQAcNNBoJkyd1x7XKm/hRb0JQJ19EjwqBv58MWQaGDe2Mcb8NxbenWudkuaTRKG6tndMMxWKx2F6//PLLdm1Go/HejaiF6PfqQgxY+vManuEJcbiwcgMuvu54KUe/bO3mi0h9KQIZOx+F/icj1m6+iIKiOgy5vxPeWR6FCc/kAgA2fXAZf5zfD5+8Nww+PjIKimrx6ptnGg9iBeInheFPiQMgy0B5RT3Wv38Jx75pnZOy8IKgocTpeij79u3DxIkTYbVaERgYaNt/+fJlbNu2DatWrWryL+N6KOrgeijqaep6KIYdbyi2+z/7V7eP1dy6PEq1dwwGA1JTU3HmzBloNBokJydjzJgxLsfkNEN56qmnAACTJ0/G/v37bfv79u2L/Px8t0+aiG4jMENpbl0ewHntnW3btiEwMBBHjhxBYWEhEhISkJ2djYCAAIe+t3M6h2IymWAwGGCxWFBfXw+DwQCDwYCKigoYDAZnbyMiBZIkK27uEl2X506HDh3C1KlTAQC9evVCZGQkjh496vJ9TjOULVu2YOPGjZAkCQ8++KBtf2BgIGbPnu3ywET0C1xkKM6e4bpzNXxRdXmc1d4pLS1F9+7dbf10Oh3Ky11/f8ppQElKSkJSUhJWrlyJpUuXujwQEbnBxR1Sdwt9uUupLs/d1t5R4vLRewYTIoE0yn9y7hb6ElGXR6n2TlhYGEpKSmzZTllZGYYPH+7y9Lz7OWCilkaWFDdnz3DdGVBE1OVRqr0TExOD3bt3AwAKCwtx+vRpu0LqzvDLgURqkltOXR6l2jtz585FSkoKxo8fD1mWsXLlSrvHR5xhXR4vxOdQ1NPU51DqMzcrtvs9sbA5w/E4ZihEahKYobREDChEamIZDSIShhkKEQnj5V8OZEAhUpHVC9aNVcKAQqQm2bv/5Lz77IhaGGYoRCQOJ2WJSBjeNiYiUazMUIhIFGtbrctDROIxQyEicXiXh4hEYYZCROJwDoWIRLFIzFCISBCRlzzNLfS1adMmZGVl2VZsW7x4sW2Zx5SUFHz99de2BatjYmKwcKHrxZ8YUIhUJPK2cXMLfQ0ZMgRz5syBv78/zp07hxkzZiA3Nxd+fo2F6OfPn48ZM2Y0aUzefUFH1MJYZY3i5i4Rhb5GjRplW182IiICVqsV1dXNK0LPDIVIRVYo3zZWu9DXLZ9++il69uyJbt262fZt374du3fvRo8ePfDKK6+gb9++Ls+PAYVIRa6yEDULfd3yzTff4B//+Ac++OAD277FixcjJCQEsizj008/xfPPP4+cnByH996JAYVIRVYXswxqFvoCgJMnT+LPf/4z3n33XfTp08e2v2vXrrbX8fHxeOutt1BeXm5XnvSXMKAQqcjiIkO589LGmdsLfcXFxSkW+urYsSN8fHxshb7Wr18PAMjLy8PixYuxfv16DB482O59V69etQWVr776CrIs2wUZZxhQiFTkag6lKZpb6GvFihWor6+3Kze8Zs0aREREIDk5GZWVlZAkCYGBgdi8eTN8fFyHCxb68kIs9KWephb6KrlwWrG9+4Co5gzH45ihEKlIZIbSEjGgEKmIj94TkTBcYImIhGGGIhAnC9WReni+p4fQhpxvUm/OoRCRMBYv//ocAwqRiizgJQ8RCcJLHiIShpc8RCSM1coMhYgEYYZCRMJwDoWIhDFbmaEQkSDMUIhIGAszFCISxdvv8nh3uCRqYSxWWXFrioKCAkydOhUTJ07E1KlTUVhY6NDn2rVrWLhwIWJjY/H444/jwIEDtjaz2YwVK1Zg3LhxGD9+PNLT091qU8IMhUhFFoHHam6hr4yMDBQVFSE7OxvV1dWIj4/HiBEjEB4ertimhBkKkYpcZSh6vR7FxcUO2521ekQU+srKysKUKVMgyzKCg4Mxbtw4HD582GWbEmYoRCpydVmTlvaBW3V5RBT6KisrQ1hYmK2vTqdDeXm5yzYlDChEKrK4WBLe3bo87nKn0JdIDChEKnKVoWi1AW4FDxGFvnQ6HUpLSzFkyBAA9lmJUpsSzqEQqchqVd7cdXuhLwCKhb5MJhMA2Ap93Zp3iYmJQXp6OiwWC6qqqpCTk4OJEye6bFPCDIVIRSIfvW9uoa+4uDicOnUKEyZMAAC88MIL6NGjh8s2JaoW+hoZ+6Vav6pN45qy6vkPY9PWlD1y6qZi+/gH2jdnOB7HDIVIRWYvf1KWAYVIRd7+6D0DCpGKmKEQkTDqzVh6BgMKkYrMFmYoRCQIMxQiEoZzKEQkjEXk+gUtEAMKkYoszFCISBRmKEQkjKvlC1o7BhQiFVl425iIRGGGQkTCcA6FiIRhhkJEwpjN4o5VUFCAlJQUVFdXIygoCKtXr0avXr3s+lRWViI1NRVlZWUwmUwYPnw4/vrXv8LHxwdLlizB+fM/r+dy/vx5bNq0CdHR0diwYQN27dqF0NBQAMBDDz2EZcuWuRxTmwwoHQN9kPpiBH49tDN+1Bvx3kcFOPJlhUM/Xx8JL83vh9/+pgt8fCScztfjb5su4HpVAwDgtZcH4uEHguDvp0HVjQZ8vO8HZGa7XhmcfvarxASEz3wKHSMHoHR3JvLmpnp6SPeUyEfv3anLs2XLFvTt2xdbt26F0WjE9OnTkZ2djUmTJmHNmjW2fufOncOsWbNsJTcAID4+HsnJyU0aU5tcU/aVBf1hNFnw5LNfY+XafLyysD969+zg0G/Kk+GIHKjFrBe/Rfys4/ipxojFf+hna9+5pwhT5p7AxKnHkPzG/2HejN6I6Buo5qm0ejdLK3Bp1bso/nCvp4eiCrNFeXOXu3V5JElCbW0tLBYLGhoaYDQa0bVrV4fj7dmzB7GxsWjXrl2zzq/NBRS/9jJ+92gX/HNnIQz1FuSd1SP3m0pMHOP4Ieu6+uHE9zdwo9qIBqMVn391Db17BtjaC4rqYDQ1/stpXGTYiu46f9XOxRuUf3oEV//1ORoqqz09FFVYrVbFzd1CX0p1eW6XmJiIgoICjBw50rY9/PDDdn0aGhqQkZGBp59+2m7/wYMHERsbizlz5uDkyZNunV+TAsrSpUub0r1F6tG9A8wWK34oNdj2XS6osQsUt2QeKUPUIC3uC26H9u1lTBgdin9/Z/8f4JWF/ZCzZyT+a8swVN5owPFvK+/5OVDrZTYrb2lpaYiOjnbY0tLS7ur3HT58GBEREcjNzcXRo0fx7bffOlQAzMnJQVhYGAYNGmTbN23aNHz++efIyMjA3LlzkZiYiBs3brj8fU7nUG6/vrrl0KFDCAxsTOmXLFni9KB6vd4horYU/n4a1NbZz4zV1JrQwd+x+FFxqQEV12/iQNoImMxWXCmswboteXZ91m6+hL+/dwmRA7UYGhmEBqOXT+NTs7iaQ3G30Je7dXl27tyJVatWQZZldOzYEWPHjsWJEycQExNj67N3716H7CQkJMT2+rHHHoNOp8PFixcxbNgwxfE7zVB27dqFiooKdOjQwbYBsHvtjLMo2xIY6s0I6GAfPAI6+KDO4Dj9/vKC/mjnK+Px3x/D+P/8Cl8ev461y6Mc+lksQN5ZPUK6tMfkSa6LIVHb5WoORavVIjw83GG7M6C4W5cnPDwcR48eBdB4aXP8+HH079/f1l5eXo7vvvsOsbGxdu+7evWq7XV+fj5KSkrQu3dvl+fnNEPZt28fli1bhsGDB+O5556DJEnYt28fkpKSXB7UWZSdtvCyy/feaz+U1EEjSwjX+aO4rPGyp1/vABQU1Tr07d8nEFt3FOCnmsZCSXszSzBvRm900vrgR73Job9GI6F7N797ewLUqpnNrjJY9x/Nd6cuz6uvvoply5YhNjYWZrMZw4cPxzPPPGM7xv79+zFmzBh06tTJ7tjr1q3DmTNnIMsyfH19sWbNGrusxRmnAaVPnz5IS0vD1q1bMXPmTCxfvhyS5N7JarVaJ+UUPR9Q6m9a8OXx63g+oRfe3nAe/fsEYuTwLli4xHHSKf/iT4gZ2xUnT1ej/qYFkyd1x7XKm/hRb0JQJ188PCQIX/9vJW42WPDIA50x7rehWP63fA+cVeslaTSQfDSQNDIkjQZy+3awmsywinxgowURedu4b9++SE9Pd9j//vvv21737NkT27dvd3qMhQsX/uL+W8GpqRSfQ5FlGTNmzMDo0aORmpqKuro6AEBNTY1tLqU1Wrv5IlJfikDGzkeh/8mItZsvoqCoDkPu74R3lkdhwjO5AIBNH1zGH+f3wyfvDYOPj4yColq8+uaZxoNYgfhJYfhT4gDIMlBeUY/171/CsW84KdsU/V5diAFLF9l+Dk+Iw4WVG3Dx9Y0eHNW94zpDad1cVg6cPHky9u/fD5PJhGvXrkGn09n2NRUrB6qDlQPV09TKgW9+opx5/WWa482B1sRphmIymWA0GmGxWFBfXw+r1YqgoCBUVFTAYDA4exsRKfD2DMVpQNmyZQs2btwISZLw4IMP2vYHBgZi9uzZqgyOyNuoWErcI5wGlKSkJCQlJWHlypVe8UAbUUvQZjOUWxhMiMTh8gVEJIylrWcoRCSOxctTFAYUIhUxQyEiYSxt9S4PEYlnbsoqSq0QAwqRinjJQ0TC8JKHiISxmHjJQ0SCePldYwYUIjVZOClLRKKIfLCtuYW+lIp5GQwGpKam4syZM9BoNEhOTsaYMWNcjokBhUhFIjOU5hb6ApwX89q2bRsCAwNx5MgRFBYWIiEhAdnZ2QgIcKwOcbs2V5eHyJOsFqvi5m5dHtGFvu506NAhTJ06FQDQq1cvREZG2ha7VsIMhUhFrh5sS0tLw8aNjstfJiUlYdGin5fKVCr0dfvK94mJiVi0aBFGjhwJg8GAhIQEu0JfBw8eRG5uLkJCQrBo0SIMHToUAFBaWoru3bvb+ul0OpSXuy6zy4BCpCJXCyy5W5fHXbcKfaWlpaG2thbz5s3D4cOHERMTg2nTpmHBggXw9fXFsWPHkJiYiKysLHTu3PmufhfASx4iVZlNFsXN3bo8txf6AqBY6OvJJ590KPQFNBbz8vX1BWBfzAsAwsLCUFJSYjtOWVkZunXr5vL8GFCIVOSqtrG7RBT6UirmFRMTg927dwMACgsLcfr0aYwaNcrluHjJQ6Qis0lcvaHmFvpSKuY1d+5cpKSkYPz48ZBlGStXrnSrdI7LMhoisYyGOlhGQz1NLaMxZXGBYnv6312X+2zJmKEQqcjq5c/eM6AQqcjspSVWb2FAIVIRMxQiEoYZChEJwwyFiISxMEMhIlGYoRCRMJxDISJhrBau2EZEgnAOhYiEYW1jIhLGIvDLgS0RAwqRirz9kkfVbxu3Nnq9HmlpaZg1a9Zdr5hF7uFn7R24wJICvV6PjRs3OiwQTOLxs/YODChEJAwDChEJw4BCRMIwoBCRMAwoCrRaLZKSknjXQQX8rL0DbxsTkTDMUIhIGAYUIhKGAeUOGzZsQENDAwBg7969iI2NRVxcHGJjY/HRRx95eHTe5fbP+pYrV67ggQcesBWtotaFcyh3iIiIwPfff4+AgADU1NQgICAAkiShpqYGsbGx2Lx5MwYOHOjpYXqF2z9roHHxoeeeew6hoaEIDQ1FcnKyh0dITcUvB95mxYoVAIBp06ZBlmXs2LEDkiQBAOrr62E0Gm0/U/P80mf98ccfY/To0airq0NdXZ2HR0h3xUp2BgwYYK2pqbH9nJOTY500aZI1MjLSun37ds8NzAvd/lnn5+dbp0+fbjWZTNb169db3377bQ+Pju4G51BciI6OxsGDB/HZZ5/hwIEDuHLliqeH5HWMRiNee+01rFixAhqNxtPDoWbgJY+bwsLCEBUVhS+++AJ9+vTx9HC8yrVr11BUVIT58xuLvOv1elitVtTU1OD111/38OioKRhQ7nD7ZOzly5fRt29fAEBVVRVOnDiBCRMmeHiE3uPWZx0WFoYTJ07Y9m/YsAF1dXWclG2FGFDuMGfOHMycORN+fn64//77kZeXBx8fH1itVsyYMQMjR4709BC9xu2f9Y4dO/jYvRfgbWMiEoaTskQkDAMKEQnDgEJEwjCgEJEwDChEJAwDChEJw4BCRMIwoBCRMP8PXZ1645iMgnAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yKQfFMyaPXi"
      },
      "source": [
        "## Identificar assets con valores nulos en t1 y t2\n",
        "\n",
        "_49 de las 83 instalaciones o assets tienen valores nulos en el timpo de medición 1 y 2_"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DCd4rwhaPXi",
        "outputId": "e35a07ee-feec-4dc7-87a4-7cf0d380577b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "assets_sin_t1t2 = df_nulos.asset_id.unique()  #identificación de assets sin t1 y t2\n",
        "print(assets_sin_t1t2.shape)\n",
        "assets_sin_t1t2"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(49,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  2,  4,  5,  6,  8, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21,\n",
              "       22, 23, 25, 26, 27, 29, 30, 32, 33, 37, 39, 40, 44, 47, 48, 49, 51,\n",
              "       52, 53, 55, 59, 61, 65, 66, 67, 70, 71, 73, 75, 78, 80, 82])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oB5mcpHCaPXk",
        "outputId": "2939a9f4-8f92-4778-8365-f720874e0166",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "assets=X_train.asset_id.unique()\n",
        "assets"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
              "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
              "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
              "       52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n",
              "       69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaaCaGY2aPXl",
        "outputId": "0a34eac5-8c93-4f78-8ccd-f12283bb4674",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "assets_con_t1t2=assets[np.isin(assets,assets_sin_t1t2, invert=True)]   #asset con las cuatro mediciones\n",
        "print(assets_con_t1t2.shape)\n",
        "assets_con_t1t2"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(34,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3,  7,  9, 14, 24, 28, 31, 34, 35, 36, 38, 41, 42, 43, 45, 46, 50,\n",
              "       54, 56, 57, 58, 60, 62, 63, 64, 68, 69, 72, 74, 76, 77, 79, 81, 83])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OGv7MOjTaPXn"
      },
      "source": [
        "_Guardar tipo de asset según si tiene las 4 mediciones o solo 2 (t3 y t4)_\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4PC_U2pyaPXo",
        "outputId": "c74ea171-e3f5-4f80-bb0f-7c0009698c95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "X_train['total_t'] = np.where(X_train.t1.isnull(),\"2\",\"4\")\n",
        "(pd.crosstab(X_train.group_id, X_train.total_t, margins=True)/728).astype('int32')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>total_t</th>\n",
              "      <th>2</th>\n",
              "      <th>4</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>group_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>29</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>49</td>\n",
              "      <td>34</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "total_t    2   4  All\n",
              "group_id             \n",
              "2         22   5   27\n",
              "3         27  29   56\n",
              "All       49  34   83"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3C9x0d_haPXp"
      },
      "source": [
        "# Preparar df data con series de tiempo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJQj1ILFaPXq",
        "outputId": "d4e18b07-f73c-47a2-8b2d-7fd0829d1fbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "data=X_train\n",
        "data['week_day']= data['week'].astype(str)+'_'+data['weekday'].astype(str)\n",
        "data=data.set_index('asset_id')\n",
        "data.head(3)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>group_id</th>\n",
              "      <th>week</th>\n",
              "      <th>weekday</th>\n",
              "      <th>t1</th>\n",
              "      <th>t2</th>\n",
              "      <th>t3</th>\n",
              "      <th>t4</th>\n",
              "      <th>total_t</th>\n",
              "      <th>week_day</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>asset_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1_3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          group_id  week  weekday  t1  t2    t3   t4 total_t week_day\n",
              "asset_id                                                             \n",
              "1                2     1        1 NaN NaN  19.0  0.0       2      1_1\n",
              "1                2     1        2 NaN NaN   8.0  0.0       2      1_2\n",
              "1                2     1        3 NaN NaN   9.0  0.0       2      1_3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDEHCuRAaPXs",
        "outputId": "73024c9f-4312-4204-ebcf-92a8be815078",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "data.groupby(data.index).size()  #número de observaciones en cada asset"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "asset_id\n",
              "1     728\n",
              "2     728\n",
              "3     728\n",
              "4     728\n",
              "5     728\n",
              "     ... \n",
              "79    728\n",
              "80    728\n",
              "81    728\n",
              "82    728\n",
              "83    728\n",
              "Length: 83, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AyBxflzaPXu",
        "outputId": "9409165b-4357-4a11-de7b-20fbe2a307db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "fecha_table = {\n",
        "    \"week_day\": data.loc[1]['week_day'].reset_index(drop=True), \"date\": pd.date_range('2018-01-01', periods=728 , freq='D') }\n",
        "fecha=pd.DataFrame(fecha_table)\n",
        "\n",
        "dia_tabla={\"week_day\": data.loc[1]['week_day'].reset_index(drop=True), \"day\":np.arange(1,729,1)}\n",
        "dia=pd.DataFrame(dia_tabla)\n",
        "\n",
        "data=data.reset_index()\n",
        "data=pd.merge(data,fecha, how='left', on='week_day')\n",
        "data=pd.merge(data,dia, how='left', on='week_day')\n",
        "data"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>asset_id</th>\n",
              "      <th>group_id</th>\n",
              "      <th>week</th>\n",
              "      <th>weekday</th>\n",
              "      <th>t1</th>\n",
              "      <th>t2</th>\n",
              "      <th>t3</th>\n",
              "      <th>t4</th>\n",
              "      <th>total_t</th>\n",
              "      <th>week_day</th>\n",
              "      <th>date</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1_1</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1_2</td>\n",
              "      <td>2018-01-02</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1_3</td>\n",
              "      <td>2018-01-03</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1_4</td>\n",
              "      <td>2018-01-04</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1_5</td>\n",
              "      <td>2018-01-05</td>\n",
              "      <td>5</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60419</th>\n",
              "      <td>83</td>\n",
              "      <td>3</td>\n",
              "      <td>104</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4</td>\n",
              "      <td>104_3</td>\n",
              "      <td>2019-12-25</td>\n",
              "      <td>724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60420</th>\n",
              "      <td>83</td>\n",
              "      <td>3</td>\n",
              "      <td>104</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>104_4</td>\n",
              "      <td>2019-12-26</td>\n",
              "      <td>725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60421</th>\n",
              "      <td>83</td>\n",
              "      <td>3</td>\n",
              "      <td>104</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4</td>\n",
              "      <td>104_5</td>\n",
              "      <td>2019-12-27</td>\n",
              "      <td>726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60422</th>\n",
              "      <td>83</td>\n",
              "      <td>3</td>\n",
              "      <td>104</td>\n",
              "      <td>6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4</td>\n",
              "      <td>104_6</td>\n",
              "      <td>2019-12-28</td>\n",
              "      <td>727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60423</th>\n",
              "      <td>83</td>\n",
              "      <td>3</td>\n",
              "      <td>104</td>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>104_7</td>\n",
              "      <td>2019-12-29</td>\n",
              "      <td>728</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>60424 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       asset_id  group_id  week  weekday  ...  total_t  week_day       date  day\n",
              "0             1         2     1        1  ...        2       1_1 2018-01-01    1\n",
              "1             1         2     1        2  ...        2       1_2 2018-01-02    2\n",
              "2             1         2     1        3  ...        2       1_3 2018-01-03    3\n",
              "3             1         2     1        4  ...        2       1_4 2018-01-04    4\n",
              "4             1         2     1        5  ...        2       1_5 2018-01-05    5\n",
              "...         ...       ...   ...      ...  ...      ...       ...        ...  ...\n",
              "60419        83         3   104        3  ...        4     104_3 2019-12-25  724\n",
              "60420        83         3   104        4  ...        4     104_4 2019-12-26  725\n",
              "60421        83         3   104        5  ...        4     104_5 2019-12-27  726\n",
              "60422        83         3   104        6  ...        4     104_6 2019-12-28  727\n",
              "60423        83         3   104        7  ...        4     104_7 2019-12-29  728\n",
              "\n",
              "[60424 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNBeffN_aPXw"
      },
      "source": [
        "data.to_csv('Output/X_train_formateado.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BbGotMjmaPXy",
        "outputId": "d585ce80-7c7c-4b2d-e737-a3e761febd4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 60424 entries, 0 to 60423\n",
            "Data columns (total 12 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   asset_id  60424 non-null  int64         \n",
            " 1   group_id  60424 non-null  int64         \n",
            " 2   week      60424 non-null  int64         \n",
            " 3   weekday   60424 non-null  int64         \n",
            " 4   t1        24752 non-null  float64       \n",
            " 5   t2        24752 non-null  float64       \n",
            " 6   t3        60424 non-null  float64       \n",
            " 7   t4        60424 non-null  float64       \n",
            " 8   total_t   60424 non-null  object        \n",
            " 9   week_day  60424 non-null  object        \n",
            " 10  date      60424 non-null  datetime64[ns]\n",
            " 11  day       60424 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(4), int64(5), object(2)\n",
            "memory usage: 6.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sgak_LyaPX0",
        "outputId": "4e76f714-1244-4ef3-bbe6-e0d3a2e52bc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>asset_id</th>\n",
              "      <th>group_id</th>\n",
              "      <th>week</th>\n",
              "      <th>weekday</th>\n",
              "      <th>t1</th>\n",
              "      <th>t2</th>\n",
              "      <th>t3</th>\n",
              "      <th>t4</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>60424.000000</td>\n",
              "      <td>60424.000000</td>\n",
              "      <td>60424.000000</td>\n",
              "      <td>60424.000000</td>\n",
              "      <td>24752.000000</td>\n",
              "      <td>24752.000000</td>\n",
              "      <td>60424.000000</td>\n",
              "      <td>60424.000000</td>\n",
              "      <td>60424.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>2.674699</td>\n",
              "      <td>52.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.536765</td>\n",
              "      <td>0.349548</td>\n",
              "      <td>45.185075</td>\n",
              "      <td>8.372484</td>\n",
              "      <td>364.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>23.958495</td>\n",
              "      <td>0.468491</td>\n",
              "      <td>30.021075</td>\n",
              "      <td>2.000017</td>\n",
              "      <td>5.029422</td>\n",
              "      <td>2.004578</td>\n",
              "      <td>64.641355</td>\n",
              "      <td>23.002216</td>\n",
              "      <td>210.157039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>26.750000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>182.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>52.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>364.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>63.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>78.250000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>546.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>83.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>728.000000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>728.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           asset_id      group_id  ...            t4           day\n",
              "count  60424.000000  60424.000000  ...  60424.000000  60424.000000\n",
              "mean      42.000000      2.674699  ...      8.372484    364.500000\n",
              "std       23.958495      0.468491  ...     23.002216    210.157039\n",
              "min        1.000000      2.000000  ...      0.000000      1.000000\n",
              "25%       21.000000      2.000000  ...      0.000000    182.750000\n",
              "50%       42.000000      3.000000  ...      1.000000    364.500000\n",
              "75%       63.000000      3.000000  ...      6.000000    546.250000\n",
              "max       83.000000      3.000000  ...    299.000000    728.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WfWT0-lFaPX2"
      },
      "source": [
        "## Agruparar por asset y explorar series de timpo para 1 asset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_zSX2LAnaPX2"
      },
      "source": [
        "data=data.set_index('asset_id')\n",
        "porAsset=data.groupby(data.index)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "40ccaeNyaPX4",
        "outputId": "82fc6d33-3081-42f8-de80-e193216fef85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        }
      },
      "source": [
        "medicionesXasset=porAsset.agg({'t1':'sum','t2':'sum','t3':'sum','t4':'sum'})\n",
        "medicionesXasset"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>t1</th>\n",
              "      <th>t2</th>\n",
              "      <th>t3</th>\n",
              "      <th>t4</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>asset_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18911.0</td>\n",
              "      <td>3293.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19489.0</td>\n",
              "      <td>6187.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>827.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>38694.0</td>\n",
              "      <td>5462.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30643.0</td>\n",
              "      <td>6308.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38389.0</td>\n",
              "      <td>5788.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>1981.0</td>\n",
              "      <td>293.0</td>\n",
              "      <td>103096.0</td>\n",
              "      <td>23572.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17061.0</td>\n",
              "      <td>2387.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>206.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>14421.0</td>\n",
              "      <td>2978.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>74295.0</td>\n",
              "      <td>9357.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>938.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>47517.0</td>\n",
              "      <td>13348.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>83 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              t1     t2        t3       t4\n",
              "asset_id                                  \n",
              "1            0.0    0.0   18911.0   3293.0\n",
              "2            0.0    0.0   19489.0   6187.0\n",
              "3          827.0  131.0   38694.0   5462.0\n",
              "4            0.0    0.0   30643.0   6308.0\n",
              "5            0.0    0.0   38389.0   5788.0\n",
              "...          ...    ...       ...      ...\n",
              "79        1981.0  293.0  103096.0  23572.0\n",
              "80           0.0    0.0   17061.0   2387.0\n",
              "81         206.0   31.0   14421.0   2978.0\n",
              "82           0.0    0.0   74295.0   9357.0\n",
              "83         938.0  261.0   47517.0  13348.0\n",
              "\n",
              "[83 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iehkE1nraPX7",
        "outputId": "cc7b75ec-8063-4ad3-9996-f6d749045cdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        }
      },
      "source": [
        "ASSET=3\n",
        "\n",
        "mediciones=[\"t1\",\"t2\",\"t3\",\"t4\"]\n",
        "\n",
        "fig, axes = plt.subplots(2,2, figsize=(10,8))\n",
        "\n",
        "for var, subplot in zip(mediciones, axes.flatten()):\n",
        "    sns.kdeplot(porAsset.get_group(ASSET)[var], ax=subplot)\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQOZEZI3aPX9",
        "outputId": "d3d56606-7b40-44c4-eb3f-1d7de2ecec3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "\n",
        "\n",
        "asset = porAsset.get_group(ASSET).reset_index()\n",
        "\n",
        "# Gráfica de la serie de tiempo de las 4 mediciones\n",
        "titulo='Series de tiempo de las mediciones correspondientes al asset: '+ str(ASSET)\n",
        "sns.set(rc={'figure.figsize':(16,5)}, palette=\"bright\")\n",
        "asset[mediciones].plot(linewidth=0.8, title=titulo)\n",
        "\n",
        "# Correlación entre las 4 mediciones para el asset de interes\n",
        "corr_=correlacion(asset[mediciones], grafica=True,figsize=(4,3))  \n",
        "\n",
        "\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}