├── README.md
└── Untitled6.ipynb


/README.md:
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1 | # DSBDA-Assignment:
2 | -   DSBDA-Assignment
3 | 


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/Untitled6.ipynb:
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   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "provenance": [],
   7 |       "authorship_tag": "ABX9TyP5zL+OQ0umPfW8iJfmPJgy",
   8 |       "include_colab_link": true
   9 |     },
  10 |     "kernelspec": {
  11 |       "name": "python3",
  12 |       "display_name": "Python 3"
  13 |     },
  14 |     "language_info": {
  15 |       "name": "python"
  16 |     }
  17 |   },
  18 |   "cells": [
  19 |     {
  20 |       "cell_type": "markdown",
  21 |       "metadata": {
  22 |         "id": "view-in-github",
  23 |         "colab_type": "text"
  24 |       },
  25 |       "source": [
  26 |         "<a href=\"https://colab.research.google.com/github/Shammachale0102/DSBDA-Assignment/blob/main/Untitled6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  27 |       ]
  28 |     },
  29 |     {
  30 |       "cell_type": "code",
  31 |       "source": [
  32 |         "# 1. import all required python libraries\n",
  33 |         "#import numpy as np\n",
  34 |         "import pandas as pd\n",
  35 |         "\n",
  36 |         "import matplotlib\n",
  37 |         "from matplotlib import pyplot as plt\n",
  38 |         "#matplotlib.style.use('ggplot')\n",
  39 |         "\n",
  40 |         "import seaborn as sns"
  41 |       ],
  42 |       "metadata": {
  43 |         "id": "scOyYw1CUgQW"
  44 |       },
  45 |       "execution_count": 17,
  46 |       "outputs": []
  47 |     },
  48 |     {
  49 |       "cell_type": "code",
  50 |       "source": [
  51 |         "#2. preparing to load data into colab from loacal drive\n",
  52 |         "from google.colab import files \n",
  53 |         "uploaded=files.upload()"
  54 |       ],
  55 |       "metadata": {
  56 |         "colab": {
  57 |           "base_uri": "https://localhost:8080/",
  58 |           "height": 73
  59 |         },
  60 |         "id": "uB99Y0HCU1Zu",
  61 |         "outputId": "111917f3-a174-4ad9-b6ea-f8991b3b4767"
  62 |       },
  63 |       "execution_count": 18,
  64 |       "outputs": [
  65 |         {
  66 |           "output_type": "display_data",
  67 |           "data": {
  68 |             "text/plain": [
  69 |               "<IPython.core.display.HTML object>"
  70 |             ],
  71 |             "text/html": [
  72 |               "\n",
  73 |               "     <input type=\"file\" id=\"files-e6b7fbfa-2c95-49f5-aea4-60619b75ce4f\" name=\"files[]\" multiple disabled\n",
  74 |               "        style=\"border:none\" />\n",
  75 |               "     <output id=\"result-e6b7fbfa-2c95-49f5-aea4-60619b75ce4f\">\n",
  76 |               "      Upload widget is only available when the cell has been executed in the\n",
  77 |               "      current browser session. Please rerun this cell to enable.\n",
  78 |               "      </output>\n",
  79 |               "      <script>// Copyright 2017 Google LLC\n",
  80 |               "//\n",
  81 |               "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
  82 |               "// you may not use this file except in compliance with the License.\n",
  83 |               "// You may obtain a copy of the License at\n",
  84 |               "//\n",
  85 |               "//      http://www.apache.org/licenses/LICENSE-2.0\n",
  86 |               "//\n",
  87 |               "// Unless required by applicable law or agreed to in writing, software\n",
  88 |               "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
  89 |               "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
  90 |               "// See the License for the specific language governing permissions and\n",
  91 |               "// limitations under the License.\n",
  92 |               "\n",
  93 |               "/**\n",
  94 |               " * @fileoverview Helpers for google.colab Python module.\n",
  95 |               " */\n",
  96 |               "(function(scope) {\n",
  97 |               "function span(text, styleAttributes = {}) {\n",
  98 |               "  const element = document.createElement('span');\n",
  99 |               "  element.textContent = text;\n",
 100 |               "  for (const key of Object.keys(styleAttributes)) {\n",
 101 |               "    element.style[key] = styleAttributes[key];\n",
 102 |               "  }\n",
 103 |               "  return element;\n",
 104 |               "}\n",
 105 |               "\n",
 106 |               "// Max number of bytes which will be uploaded at a time.\n",
 107 |               "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
 108 |               "\n",
 109 |               "function _uploadFiles(inputId, outputId) {\n",
 110 |               "  const steps = uploadFilesStep(inputId, outputId);\n",
 111 |               "  const outputElement = document.getElementById(outputId);\n",
 112 |               "  // Cache steps on the outputElement to make it available for the next call\n",
 113 |               "  // to uploadFilesContinue from Python.\n",
 114 |               "  outputElement.steps = steps;\n",
 115 |               "\n",
 116 |               "  return _uploadFilesContinue(outputId);\n",
 117 |               "}\n",
 118 |               "\n",
 119 |               "// This is roughly an async generator (not supported in the browser yet),\n",
 120 |               "// where there are multiple asynchronous steps and the Python side is going\n",
 121 |               "// to poll for completion of each step.\n",
 122 |               "// This uses a Promise to block the python side on completion of each step,\n",
 123 |               "// then passes the result of the previous step as the input to the next step.\n",
 124 |               "function _uploadFilesContinue(outputId) {\n",
 125 |               "  const outputElement = document.getElementById(outputId);\n",
 126 |               "  const steps = outputElement.steps;\n",
 127 |               "\n",
 128 |               "  const next = steps.next(outputElement.lastPromiseValue);\n",
 129 |               "  return Promise.resolve(next.value.promise).then((value) => {\n",
 130 |               "    // Cache the last promise value to make it available to the next\n",
 131 |               "    // step of the generator.\n",
 132 |               "    outputElement.lastPromiseValue = value;\n",
 133 |               "    return next.value.response;\n",
 134 |               "  });\n",
 135 |               "}\n",
 136 |               "\n",
 137 |               "/**\n",
 138 |               " * Generator function which is called between each async step of the upload\n",
 139 |               " * process.\n",
 140 |               " * @param {string} inputId Element ID of the input file picker element.\n",
 141 |               " * @param {string} outputId Element ID of the output display.\n",
 142 |               " * @return {!Iterable<!Object>} Iterable of next steps.\n",
 143 |               " */\n",
 144 |               "function* uploadFilesStep(inputId, outputId) {\n",
 145 |               "  const inputElement = document.getElementById(inputId);\n",
 146 |               "  inputElement.disabled = false;\n",
 147 |               "\n",
 148 |               "  const outputElement = document.getElementById(outputId);\n",
 149 |               "  outputElement.innerHTML = '';\n",
 150 |               "\n",
 151 |               "  const pickedPromise = new Promise((resolve) => {\n",
 152 |               "    inputElement.addEventListener('change', (e) => {\n",
 153 |               "      resolve(e.target.files);\n",
 154 |               "    });\n",
 155 |               "  });\n",
 156 |               "\n",
 157 |               "  const cancel = document.createElement('button');\n",
 158 |               "  inputElement.parentElement.appendChild(cancel);\n",
 159 |               "  cancel.textContent = 'Cancel upload';\n",
 160 |               "  const cancelPromise = new Promise((resolve) => {\n",
 161 |               "    cancel.onclick = () => {\n",
 162 |               "      resolve(null);\n",
 163 |               "    };\n",
 164 |               "  });\n",
 165 |               "\n",
 166 |               "  // Wait for the user to pick the files.\n",
 167 |               "  const files = yield {\n",
 168 |               "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
 169 |               "    response: {\n",
 170 |               "      action: 'starting',\n",
 171 |               "    }\n",
 172 |               "  };\n",
 173 |               "\n",
 174 |               "  cancel.remove();\n",
 175 |               "\n",
 176 |               "  // Disable the input element since further picks are not allowed.\n",
 177 |               "  inputElement.disabled = true;\n",
 178 |               "\n",
 179 |               "  if (!files) {\n",
 180 |               "    return {\n",
 181 |               "      response: {\n",
 182 |               "        action: 'complete',\n",
 183 |               "      }\n",
 184 |               "    };\n",
 185 |               "  }\n",
 186 |               "\n",
 187 |               "  for (const file of files) {\n",
 188 |               "    const li = document.createElement('li');\n",
 189 |               "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
 190 |               "    li.append(span(\n",
 191 |               "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
 192 |               "        `last modified: ${\n",
 193 |               "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
 194 |               "                                    'n/a'} - `));\n",
 195 |               "    const percent = span('0% done');\n",
 196 |               "    li.appendChild(percent);\n",
 197 |               "\n",
 198 |               "    outputElement.appendChild(li);\n",
 199 |               "\n",
 200 |               "    const fileDataPromise = new Promise((resolve) => {\n",
 201 |               "      const reader = new FileReader();\n",
 202 |               "      reader.onload = (e) => {\n",
 203 |               "        resolve(e.target.result);\n",
 204 |               "      };\n",
 205 |               "      reader.readAsArrayBuffer(file);\n",
 206 |               "    });\n",
 207 |               "    // Wait for the data to be ready.\n",
 208 |               "    let fileData = yield {\n",
 209 |               "      promise: fileDataPromise,\n",
 210 |               "      response: {\n",
 211 |               "        action: 'continue',\n",
 212 |               "      }\n",
 213 |               "    };\n",
 214 |               "\n",
 215 |               "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
 216 |               "    let position = 0;\n",
 217 |               "    do {\n",
 218 |               "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
 219 |               "      const chunk = new Uint8Array(fileData, position, length);\n",
 220 |               "      position += length;\n",
 221 |               "\n",
 222 |               "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
 223 |               "      yield {\n",
 224 |               "        response: {\n",
 225 |               "          action: 'append',\n",
 226 |               "          file: file.name,\n",
 227 |               "          data: base64,\n",
 228 |               "        },\n",
 229 |               "      };\n",
 230 |               "\n",
 231 |               "      let percentDone = fileData.byteLength === 0 ?\n",
 232 |               "          100 :\n",
 233 |               "          Math.round((position / fileData.byteLength) * 100);\n",
 234 |               "      percent.textContent = `${percentDone}% done`;\n",
 235 |               "\n",
 236 |               "    } while (position < fileData.byteLength);\n",
 237 |               "  }\n",
 238 |               "\n",
 239 |               "  // All done.\n",
 240 |               "  yield {\n",
 241 |               "    response: {\n",
 242 |               "      action: 'complete',\n",
 243 |               "    }\n",
 244 |               "  };\n",
 245 |               "}\n",
 246 |               "\n",
 247 |               "scope.google = scope.google || {};\n",
 248 |               "scope.google.colab = scope.google.colab || {};\n",
 249 |               "scope.google.colab._files = {\n",
 250 |               "  _uploadFiles,\n",
 251 |               "  _uploadFilesContinue,\n",
 252 |               "};\n",
 253 |               "})(self);\n",
 254 |               "</script> "
 255 |             ]
 256 |           },
 257 |           "metadata": {}
 258 |         },
 259 |         {
 260 |           "output_type": "stream",
 261 |           "name": "stdout",
 262 |           "text": [
 263 |             "Saving iris_dataset.csv to iris_dataset (1).csv\n"
 264 |           ]
 265 |         }
 266 |       ]
 267 |     },
 268 |     {
 269 |       "cell_type": "code",
 270 |       "source": [
 271 |         "# 3. Loading data into dataframe\n",
 272 |         "import io \n",
 273 |         "df=pd.read_csv(io.BytesIO(uploaded['iris_dataset.csv']))   #import csv file in dataframe called df\n",
 274 |         "# Dataset is now stored in a pandas Dataframe"
 275 |       ],
 276 |       "metadata": {
 277 |         "id": "u-LxVkH5U883"
 278 |       },
 279 |       "execution_count": 19,
 280 |       "outputs": []
 281 |     },
 282 |     {
 283 |       "cell_type": "code",
 284 |       "source": [
 285 |         "#classes = df['Species'].unique().tolist()\n",
 286 |         "#classes"
 287 |       ],
 288 |       "metadata": {
 289 |         "id": "KvlKKoMYVCGG"
 290 |       },
 291 |       "execution_count": 20,
 292 |       "outputs": []
 293 |     },
 294 |     {
 295 |       "cell_type": "code",
 296 |       "source": [
 297 |         "#4.1 Information of the data present in dataset\n",
 298 |         "df.info()"
 299 |       ],
 300 |       "metadata": {
 301 |         "colab": {
 302 |           "base_uri": "https://localhost:8080/"
 303 |         },
 304 |         "id": "EUlhTdxOVFzF",
 305 |         "outputId": "7a128231-ae1d-4241-9143-174993af4a91"
 306 |       },
 307 |       "execution_count": 21,
 308 |       "outputs": [
 309 |         {
 310 |           "output_type": "stream",
 311 |           "name": "stdout",
 312 |           "text": [
 313 |             "<class 'pandas.core.frame.DataFrame'>\n",
 314 |             "RangeIndex: 150 entries, 0 to 149\n",
 315 |             "Data columns (total 5 columns):\n",
 316 |             " #   Column             Non-Null Count  Dtype  \n",
 317 |             "---  ------             --------------  -----  \n",
 318 |             " 0   sepal length (cm)  150 non-null    float64\n",
 319 |             " 1   sepal width (cm)   150 non-null    float64\n",
 320 |             " 2   petal length (cm)  150 non-null    float64\n",
 321 |             " 3   petal width (cm)   150 non-null    float64\n",
 322 |             " 4   target             150 non-null    object \n",
 323 |             "dtypes: float64(4), object(1)\n",
 324 |             "memory usage: 6.0+ KB\n"
 325 |           ]
 326 |         }
 327 |       ]
 328 |     },
 329 |     {
 330 |       "cell_type": "code",
 331 |       "source": [
 332 |         "#4.2 Getting the dimension/shape of the data.\n",
 333 |         "# Expect it to be 150 rows and 5 columns.\n",
 334 |         "\n",
 335 |         "print(df.shape)"
 336 |       ],
 337 |       "metadata": {
 338 |         "colab": {
 339 |           "base_uri": "https://localhost:8080/"
 340 |         },
 341 |         "id": "jFrx7TVjVJFs",
 342 |         "outputId": "8a663e60-cc7e-4ee5-bdeb-4c9b4d3d90ed"
 343 |       },
 344 |       "execution_count": 22,
 345 |       "outputs": [
 346 |         {
 347 |           "output_type": "stream",
 348 |           "name": "stdout",
 349 |           "text": [
 350 |             "(150, 5)\n"
 351 |           ]
 352 |         }
 353 |       ]
 354 |     },
 355 |     {
 356 |       "cell_type": "code",
 357 |       "source": [
 358 |         "# print the first 20 data points--the head of the dataset\n",
 359 |         "print(df.head(10))"
 360 |       ],
 361 |       "metadata": {
 362 |         "colab": {
 363 |           "base_uri": "https://localhost:8080/"
 364 |         },
 365 |         "id": "Gc90dSDcVMi0",
 366 |         "outputId": "cd94bdfe-9331-4e72-92e9-e234b44108bb"
 367 |       },
 368 |       "execution_count": 23,
 369 |       "outputs": [
 370 |         {
 371 |           "output_type": "stream",
 372 |           "name": "stdout",
 373 |           "text": [
 374 |             "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
 375 |             "0                5.1               3.5                1.4               0.2   \n",
 376 |             "1                4.9               3.0                1.4               0.2   \n",
 377 |             "2                4.7               3.2                1.3               0.2   \n",
 378 |             "3                4.6               3.1                1.5               0.2   \n",
 379 |             "4                5.0               3.6                1.4               0.2   \n",
 380 |             "5                5.4               3.9                1.7               0.4   \n",
 381 |             "6                4.6               3.4                1.4               0.3   \n",
 382 |             "7                5.0               3.4                1.5               0.2   \n",
 383 |             "8                4.4               2.9                1.4               0.2   \n",
 384 |             "9                4.9               3.1                1.5               0.1   \n",
 385 |             "\n",
 386 |             "        target  \n",
 387 |             "0  Iris-setosa  \n",
 388 |             "1  Iris-setosa  \n",
 389 |             "2  Iris-setosa  \n",
 390 |             "3  Iris-setosa  \n",
 391 |             "4  Iris-setosa  \n",
 392 |             "5  Iris-setosa  \n",
 393 |             "6  Iris-setosa  \n",
 394 |             "7  Iris-setosa  \n",
 395 |             "8  Iris-setosa  \n",
 396 |             "9  Iris-setosa  \n"
 397 |           ]
 398 |         }
 399 |       ]
 400 |     },
 401 |     {
 402 |       "cell_type": "code",
 403 |       "source": [
 404 |         "#4.3 Describing the data present in dataset\n",
 405 |         "# Use the describe function to describe same of the\n",
 406 |         "print(df.describe())"
 407 |       ],
 408 |       "metadata": {
 409 |         "colab": {
 410 |           "base_uri": "https://localhost:8080/"
 411 |         },
 412 |         "id": "uOJiXby8VP9k",
 413 |         "outputId": "b4008e03-3184-4ce5-c44e-97b0fe2ba5a8"
 414 |       },
 415 |       "execution_count": 24,
 416 |       "outputs": [
 417 |         {
 418 |           "output_type": "stream",
 419 |           "name": "stdout",
 420 |           "text": [
 421 |             "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
 422 |             "count         150.000000        150.000000         150.000000   \n",
 423 |             "mean            5.843333          3.054000           3.758667   \n",
 424 |             "std             0.828066          0.433594           1.764420   \n",
 425 |             "min             4.300000          2.000000           1.000000   \n",
 426 |             "25%             5.100000          2.800000           1.600000   \n",
 427 |             "50%             5.800000          3.000000           4.350000   \n",
 428 |             "75%             6.400000          3.300000           5.100000   \n",
 429 |             "max             7.900000          4.400000           6.900000   \n",
 430 |             "\n",
 431 |             "       petal width (cm)  \n",
 432 |             "count        150.000000  \n",
 433 |             "mean           1.198667  \n",
 434 |             "std            0.763161  \n",
 435 |             "min            0.100000  \n",
 436 |             "25%            0.300000  \n",
 437 |             "50%            1.300000  \n",
 438 |             "75%            1.800000  \n",
 439 |             "max            2.500000  \n"
 440 |           ]
 441 |         }
 442 |       ]
 443 |     },
 444 |     {
 445 |       "cell_type": "code",
 446 |       "source": [
 447 |         "#4.4 check the data types in the dataframe\n",
 448 |         "df.dtypes"
 449 |       ],
 450 |       "metadata": {
 451 |         "colab": {
 452 |           "base_uri": "https://localhost:8080/"
 453 |         },
 454 |         "id": "2XHIJP-bVWSO",
 455 |         "outputId": "83b06a1b-26da-41ce-f47a-14e8a87d668c"
 456 |       },
 457 |       "execution_count": 25,
 458 |       "outputs": [
 459 |         {
 460 |           "output_type": "execute_result",
 461 |           "data": {
 462 |             "text/plain": [
 463 |               "sepal length (cm)    float64\n",
 464 |               "sepal width (cm)     float64\n",
 465 |               "petal length (cm)    float64\n",
 466 |               "petal width (cm)     float64\n",
 467 |               "target                object\n",
 468 |               "dtype: object"
 469 |             ]
 470 |           },
 471 |           "metadata": {},
 472 |           "execution_count": 25
 473 |         }
 474 |       ]
 475 |     },
 476 |     {
 477 |       "cell_type": "code",
 478 |       "source": [
 479 |         "df[\"target\"].unique()"
 480 |       ],
 481 |       "metadata": {
 482 |         "colab": {
 483 |           "base_uri": "https://localhost:8080/"
 484 |         },
 485 |         "id": "CQVj0Hj9VZuk",
 486 |         "outputId": "1fbed8c1-9e5f-4533-eed8-0f18843b5eba"
 487 |       },
 488 |       "execution_count": 26,
 489 |       "outputs": [
 490 |         {
 491 |           "output_type": "execute_result",
 492 |           "data": {
 493 |             "text/plain": [
 494 |               "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
 495 |             ]
 496 |           },
 497 |           "metadata": {},
 498 |           "execution_count": 26
 499 |         }
 500 |       ]
 501 |     },
 502 |     {
 503 |       "cell_type": "code",
 504 |       "source": [
 505 |         "#4.5 Use the groupby method to determine the class distribution\n",
 506 |         "print(df.groupby('target').size())"
 507 |       ],
 508 |       "metadata": {
 509 |         "colab": {
 510 |           "base_uri": "https://localhost:8080/"
 511 |         },
 512 |         "id": "ivDuBCywVdTA",
 513 |         "outputId": "9916574d-262b-40f4-d662-d2f3f1c70023"
 514 |       },
 515 |       "execution_count": 27,
 516 |       "outputs": [
 517 |         {
 518 |           "output_type": "stream",
 519 |           "name": "stdout",
 520 |           "text": [
 521 |             "target\n",
 522 |             "Iris-setosa        50\n",
 523 |             "Iris-versicolor    50\n",
 524 |             "Iris-virginica     50\n",
 525 |             "dtype: int64\n"
 526 |           ]
 527 |         }
 528 |       ]
 529 |     },
 530 |     {
 531 |       "cell_type": "code",
 532 |       "source": [
 533 |         "#4.6 Data points count value for each class labels.\n",
 534 |         "df.target.value_counts()"
 535 |       ],
 536 |       "metadata": {
 537 |         "colab": {
 538 |           "base_uri": "https://localhost:8080/"
 539 |         },
 540 |         "id": "A_OPKiYZVhox",
 541 |         "outputId": "afbbe2ec-d6a2-4106-e2c0-c01475f32854"
 542 |       },
 543 |       "execution_count": 28,
 544 |       "outputs": [
 545 |         {
 546 |           "output_type": "execute_result",
 547 |           "data": {
 548 |             "text/plain": [
 549 |               "Iris-setosa        50\n",
 550 |               "Iris-versicolor    50\n",
 551 |               "Iris-virginica     50\n",
 552 |               "Name: target, dtype: int64"
 553 |             ]
 554 |           },
 555 |           "metadata": {},
 556 |           "execution_count": 28
 557 |         }
 558 |       ]
 559 |     },
 560 |     {
 561 |       "cell_type": "code",
 562 |       "source": [
 563 |         "\n",
 564 |         "#4.5 checking for missing value\n",
 565 |         "df.isnull().sum()"
 566 |       ],
 567 |       "metadata": {
 568 |         "colab": {
 569 |           "base_uri": "https://localhost:8080/"
 570 |         },
 571 |         "id": "vZrUj29KVmT6",
 572 |         "outputId": "21f17a25-15a2-4f6c-8241-40dcf87db99c"
 573 |       },
 574 |       "execution_count": 29,
 575 |       "outputs": [
 576 |         {
 577 |           "output_type": "execute_result",
 578 |           "data": {
 579 |             "text/plain": [
 580 |               "sepal length (cm)    0\n",
 581 |               "sepal width (cm)     0\n",
 582 |               "petal length (cm)    0\n",
 583 |               "petal width (cm)     0\n",
 584 |               "target               0\n",
 585 |               "dtype: int64"
 586 |             ]
 587 |           },
 588 |           "metadata": {},
 589 |           "execution_count": 29
 590 |         }
 591 |       ]
 592 |     },
 593 |     {
 594 |       "cell_type": "code",
 595 |       "source": [
 596 |         "#6. Turning categorical variables into quantitative variables\n",
 597 |         "\n",
 598 |         "from sklearn import preprocessing\n",
 599 |         "#Label_encoder object knows how to understand word labels\n",
 600 |         "Label_encoder= preprocessing.LabelEncoder()\n",
 601 |         "\n",
 602 |         "#Encoder Labels in column 'species\n",
 603 |         "df['target']=Label_encoder.fit_transform(df['target'])\n",
 604 |         "df['target'].unique()\n",
 605 |         "     "
 606 |       ],
 607 |       "metadata": {
 608 |         "colab": {
 609 |           "base_uri": "https://localhost:8080/"
 610 |         },
 611 |         "id": "0MRbb27vVo3k",
 612 |         "outputId": "ad977fe0-d5f3-4ab6-d417-96b2f5b154d3"
 613 |       },
 614 |       "execution_count": 30,
 615 |       "outputs": [
 616 |         {
 617 |           "output_type": "execute_result",
 618 |           "data": {
 619 |             "text/plain": [
 620 |               "array([0, 1, 2])"
 621 |             ]
 622 |           },
 623 |           "metadata": {},
 624 |           "execution_count": 30
 625 |         }
 626 |       ]
 627 |     },
 628 |     {
 629 |       "cell_type": "code",
 630 |       "source": [
 631 |         "import pandas as pd"
 632 |       ],
 633 |       "metadata": {
 634 |         "id": "tVGBXIBBQuPn"
 635 |       },
 636 |       "execution_count": 31,
 637 |       "outputs": []
 638 |     },
 639 |     {
 640 |       "cell_type": "code",
 641 |       "source": [
 642 |         "csv_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'"
 643 |       ],
 644 |       "metadata": {
 645 |         "id": "J4UF3fhRQdG8"
 646 |       },
 647 |       "execution_count": 32,
 648 |       "outputs": []
 649 |     },
 650 |     {
 651 |       "cell_type": "code",
 652 |       "source": [
 653 |         "iris = pd.read_csv(csv_url, header = None)"
 654 |       ],
 655 |       "metadata": {
 656 |         "id": "O495VIXDQ3g4"
 657 |       },
 658 |       "execution_count": 33,
 659 |       "outputs": []
 660 |     },
 661 |     {
 662 |       "cell_type": "code",
 663 |       "source": [
 664 |         "#Iris doesnt have column name so give it\n",
 665 |         "col_names =['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width','Species']"
 666 |       ],
 667 |       "metadata": {
 668 |         "id": "_4gD0aJsQ7A3"
 669 |       },
 670 |       "execution_count": 34,
 671 |       "outputs": []
 672 |     },
 673 |     {
 674 |       "cell_type": "code",
 675 |       "source": [
 676 |         "iris = pd.read_csv(csv_url, names = col_names)"
 677 |       ],
 678 |       "metadata": {
 679 |         "id": "xaRiNwS4Q_H4"
 680 |       },
 681 |       "execution_count": 35,
 682 |       "outputs": []
 683 |     },
 684 |     {
 685 |       "cell_type": "code",
 686 |       "source": [
 687 |         "df1=df=iris"
 688 |       ],
 689 |       "metadata": {
 690 |         "id": "eGZHEyHERBUP"
 691 |       },
 692 |       "execution_count": 36,
 693 |       "outputs": []
 694 |     },
 695 |     {
 696 |       "cell_type": "code",
 697 |       "source": [
 698 |         "iris.head(8)"
 699 |       ],
 700 |       "metadata": {
 701 |         "colab": {
 702 |           "base_uri": "https://localhost:8080/",
 703 |           "height": 300
 704 |         },
 705 |         "id": "cJSpBviKRGjy",
 706 |         "outputId": "d0106c58-ca99-4125-b91f-3ea8160d2fea"
 707 |       },
 708 |       "execution_count": 37,
 709 |       "outputs": [
 710 |         {
 711 |           "output_type": "execute_result",
 712 |           "data": {
 713 |             "text/plain": [
 714 |               "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width      Species\n",
 715 |               "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
 716 |               "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
 717 |               "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
 718 |               "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
 719 |               "4           5.0          3.6           1.4          0.2  Iris-setosa\n",
 720 |               "5           5.4          3.9           1.7          0.4  Iris-setosa\n",
 721 |               "6           4.6          3.4           1.4          0.3  Iris-setosa\n",
 722 |               "7           5.0          3.4           1.5          0.2  Iris-setosa"
 723 |             ],
 724 |             "text/html": [
 725 |               "\n",
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 727 |               "    <div class=\"colab-df-container\">\n",
 728 |               "      <div>\n",
 729 |               "<style scoped>\n",
 730 |               "    .dataframe tbody tr th:only-of-type {\n",
 731 |               "        vertical-align: middle;\n",
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 733 |               "\n",
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 743 |               "  <thead>\n",
 744 |               "    <tr style=\"text-align: right;\">\n",
 745 |               "      <th></th>\n",
 746 |               "      <th>Sepal_Length</th>\n",
 747 |               "      <th>Sepal_Width</th>\n",
 748 |               "      <th>Petal_Length</th>\n",
 749 |               "      <th>Petal_Width</th>\n",
 750 |               "      <th>Species</th>\n",
 751 |               "    </tr>\n",
 752 |               "  </thead>\n",
 753 |               "  <tbody>\n",
 754 |               "    <tr>\n",
 755 |               "      <th>0</th>\n",
 756 |               "      <td>5.1</td>\n",
 757 |               "      <td>3.5</td>\n",
 758 |               "      <td>1.4</td>\n",
 759 |               "      <td>0.2</td>\n",
 760 |               "      <td>Iris-setosa</td>\n",
 761 |               "    </tr>\n",
 762 |               "    <tr>\n",
 763 |               "      <th>1</th>\n",
 764 |               "      <td>4.9</td>\n",
 765 |               "      <td>3.0</td>\n",
 766 |               "      <td>1.4</td>\n",
 767 |               "      <td>0.2</td>\n",
 768 |               "      <td>Iris-setosa</td>\n",
 769 |               "    </tr>\n",
 770 |               "    <tr>\n",
 771 |               "      <th>2</th>\n",
 772 |               "      <td>4.7</td>\n",
 773 |               "      <td>3.2</td>\n",
 774 |               "      <td>1.3</td>\n",
 775 |               "      <td>0.2</td>\n",
 776 |               "      <td>Iris-setosa</td>\n",
 777 |               "    </tr>\n",
 778 |               "    <tr>\n",
 779 |               "      <th>3</th>\n",
 780 |               "      <td>4.6</td>\n",
 781 |               "      <td>3.1</td>\n",
 782 |               "      <td>1.5</td>\n",
 783 |               "      <td>0.2</td>\n",
 784 |               "      <td>Iris-setosa</td>\n",
 785 |               "    </tr>\n",
 786 |               "    <tr>\n",
 787 |               "      <th>4</th>\n",
 788 |               "      <td>5.0</td>\n",
 789 |               "      <td>3.6</td>\n",
 790 |               "      <td>1.4</td>\n",
 791 |               "      <td>0.2</td>\n",
 792 |               "      <td>Iris-setosa</td>\n",
 793 |               "    </tr>\n",
 794 |               "    <tr>\n",
 795 |               "      <th>5</th>\n",
 796 |               "      <td>5.4</td>\n",
 797 |               "      <td>3.9</td>\n",
 798 |               "      <td>1.7</td>\n",
 799 |               "      <td>0.4</td>\n",
 800 |               "      <td>Iris-setosa</td>\n",
 801 |               "    </tr>\n",
 802 |               "    <tr>\n",
 803 |               "      <th>6</th>\n",
 804 |               "      <td>4.6</td>\n",
 805 |               "      <td>3.4</td>\n",
 806 |               "      <td>1.4</td>\n",
 807 |               "      <td>0.3</td>\n",
 808 |               "      <td>Iris-setosa</td>\n",
 809 |               "    </tr>\n",
 810 |               "    <tr>\n",
 811 |               "      <th>7</th>\n",
 812 |               "      <td>5.0</td>\n",
 813 |               "      <td>3.4</td>\n",
 814 |               "      <td>1.5</td>\n",
 815 |               "      <td>0.2</td>\n",
 816 |               "      <td>Iris-setosa</td>\n",
 817 |               "    </tr>\n",
 818 |               "  </tbody>\n",
 819 |               "</table>\n",
 820 |               "</div>\n",
 821 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f4aacd7-9fde-41da-af55-bed2321fe2c2')\"\n",
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 829 |               "  </svg>\n",
 830 |               "      </button>\n",
 831 |               "      \n",
 832 |               "  <style>\n",
 833 |               "    .colab-df-container {\n",
 834 |               "      display:flex;\n",
 835 |               "      flex-wrap:wrap;\n",
 836 |               "      gap: 12px;\n",
 837 |               "    }\n",
 838 |               "\n",
 839 |               "    .colab-df-convert {\n",
 840 |               "      background-color: #E8F0FE;\n",
 841 |               "      border: none;\n",
 842 |               "      border-radius: 50%;\n",
 843 |               "      cursor: pointer;\n",
 844 |               "      display: none;\n",
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 846 |               "      height: 32px;\n",
 847 |               "      padding: 0 0 0 0;\n",
 848 |               "      width: 32px;\n",
 849 |               "    }\n",
 850 |               "\n",
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 854 |               "      fill: #174EA6;\n",
 855 |               "    }\n",
 856 |               "\n",
 857 |               "    [theme=dark] .colab-df-convert {\n",
 858 |               "      background-color: #3B4455;\n",
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 861 |               "\n",
 862 |               "    [theme=dark] .colab-df-convert:hover {\n",
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 873 |               "        buttonEl.style.display =\n",
 874 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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 879 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 880 |               "                                                     [key], {});\n",
 881 |               "          if (!dataTable) return;\n",
 882 |               "\n",
 883 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
 884 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 885 |               "            + ' to learn more about interactive tables.';\n",
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 887 |               "          dataTable['output_type'] = 'display_data';\n",
 888 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
 889 |               "          const docLink = document.createElement('div');\n",
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 895 |               "  </div>\n",
 896 |               "  "
 897 |             ]
 898 |           },
 899 |           "metadata": {},
 900 |           "execution_count": 37
 901 |         }
 902 |       ]
 903 |     },
 904 |     {
 905 |       "cell_type": "code",
 906 |       "source": [
 907 |         "iris.tail()"
 908 |       ],
 909 |       "metadata": {
 910 |         "colab": {
 911 |           "base_uri": "https://localhost:8080/",
 912 |           "height": 206
 913 |         },
 914 |         "id": "6qwWkY7oRJVW",
 915 |         "outputId": "06a810e2-c895-423d-cc3e-90ee06f3913d"
 916 |       },
 917 |       "execution_count": 38,
 918 |       "outputs": [
 919 |         {
 920 |           "output_type": "execute_result",
 921 |           "data": {
 922 |             "text/plain": [
 923 |               "     Sepal_Length  Sepal_Width  Petal_Length  Petal_Width         Species\n",
 924 |               "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
 925 |               "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
 926 |               "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
 927 |               "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
 928 |               "149           5.9          3.0           5.1          1.8  Iris-virginica"
 929 |             ],
 930 |             "text/html": [
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 997 |               "      <td>1.8</td>\n",
 998 |               "      <td>Iris-virginica</td>\n",
 999 |               "    </tr>\n",
1000 |               "  </tbody>\n",
1001 |               "</table>\n",
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1043 |               "\n",
1044 |               "    [theme=dark] .colab-df-convert:hover {\n",
1045 |               "      background-color: #434B5C;\n",
1046 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1047 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1048 |               "      fill: #FFFFFF;\n",
1049 |               "    }\n",
1050 |               "  </style>\n",
1051 |               "\n",
1052 |               "      <script>\n",
1053 |               "        const buttonEl =\n",
1054 |               "          document.querySelector('#df-864b29de-d9a0-494c-8f02-291bc3486340 button.colab-df-convert');\n",
1055 |               "        buttonEl.style.display =\n",
1056 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1057 |               "\n",
1058 |               "        async function convertToInteractive(key) {\n",
1059 |               "          const element = document.querySelector('#df-864b29de-d9a0-494c-8f02-291bc3486340');\n",
1060 |               "          const dataTable =\n",
1061 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1062 |               "                                                     [key], {});\n",
1063 |               "          if (!dataTable) return;\n",
1064 |               "\n",
1065 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1066 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1067 |               "            + ' to learn more about interactive tables.';\n",
1068 |               "          element.innerHTML = '';\n",
1069 |               "          dataTable['output_type'] = 'display_data';\n",
1070 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
1071 |               "          const docLink = document.createElement('div');\n",
1072 |               "          docLink.innerHTML = docLinkHtml;\n",
1073 |               "          element.appendChild(docLink);\n",
1074 |               "        }\n",
1075 |               "      </script>\n",
1076 |               "    </div>\n",
1077 |               "  </div>\n",
1078 |               "  "
1079 |             ]
1080 |           },
1081 |           "metadata": {},
1082 |           "execution_count": 38
1083 |         }
1084 |       ]
1085 |     },
1086 |     {
1087 |       "cell_type": "code",
1088 |       "source": [
1089 |         "iris.index"
1090 |       ],
1091 |       "metadata": {
1092 |         "colab": {
1093 |           "base_uri": "https://localhost:8080/"
1094 |         },
1095 |         "id": "5gcbNQ4dRMvT",
1096 |         "outputId": "523d67b4-8846-4cbf-b291-6fc0a0a26a79"
1097 |       },
1098 |       "execution_count": 39,
1099 |       "outputs": [
1100 |         {
1101 |           "output_type": "execute_result",
1102 |           "data": {
1103 |             "text/plain": [
1104 |               "RangeIndex(start=0, stop=150, step=1)"
1105 |             ]
1106 |           },
1107 |           "metadata": {},
1108 |           "execution_count": 39
1109 |         }
1110 |       ]
1111 |     },
1112 |     {
1113 |       "cell_type": "code",
1114 |       "source": [
1115 |         "iris.columns"
1116 |       ],
1117 |       "metadata": {
1118 |         "colab": {
1119 |           "base_uri": "https://localhost:8080/"
1120 |         },
1121 |         "id": "waqwYSLPRQVB",
1122 |         "outputId": "a7bdccf6-46de-4e98-c1e1-ab9d4be1173d"
1123 |       },
1124 |       "execution_count": 40,
1125 |       "outputs": [
1126 |         {
1127 |           "output_type": "execute_result",
1128 |           "data": {
1129 |             "text/plain": [
1130 |               "Index(['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width',\n",
1131 |               "       'Species'],\n",
1132 |               "      dtype='object')"
1133 |             ]
1134 |           },
1135 |           "metadata": {},
1136 |           "execution_count": 40
1137 |         }
1138 |       ]
1139 |     },
1140 |     {
1141 |       "cell_type": "code",
1142 |       "source": [
1143 |         "iris.shape"
1144 |       ],
1145 |       "metadata": {
1146 |         "colab": {
1147 |           "base_uri": "https://localhost:8080/"
1148 |         },
1149 |         "id": "qr9VBcdvRVvo",
1150 |         "outputId": "7a3725fc-7505-4ee1-fce1-b41edd8eaa1c"
1151 |       },
1152 |       "execution_count": 41,
1153 |       "outputs": [
1154 |         {
1155 |           "output_type": "execute_result",
1156 |           "data": {
1157 |             "text/plain": [
1158 |               "(150, 5)"
1159 |             ]
1160 |           },
1161 |           "metadata": {},
1162 |           "execution_count": 41
1163 |         }
1164 |       ]
1165 |     },
1166 |     {
1167 |       "cell_type": "code",
1168 |       "source": [
1169 |         "iris.dtypes\n",
1170 |         "     "
1171 |       ],
1172 |       "metadata": {
1173 |         "colab": {
1174 |           "base_uri": "https://localhost:8080/"
1175 |         },
1176 |         "id": "8QOwlrJKRY8Y",
1177 |         "outputId": "3ca8169b-dfe8-442e-f35a-3742e06cc3f7"
1178 |       },
1179 |       "execution_count": 42,
1180 |       "outputs": [
1181 |         {
1182 |           "output_type": "execute_result",
1183 |           "data": {
1184 |             "text/plain": [
1185 |               "Sepal_Length    float64\n",
1186 |               "Sepal_Width     float64\n",
1187 |               "Petal_Length    float64\n",
1188 |               "Petal_Width     float64\n",
1189 |               "Species          object\n",
1190 |               "dtype: object"
1191 |             ]
1192 |           },
1193 |           "metadata": {},
1194 |           "execution_count": 42
1195 |         }
1196 |       ]
1197 |     },
1198 |     {
1199 |       "cell_type": "code",
1200 |       "source": [
1201 |         "\n",
1202 |         "iris.describe()"
1203 |       ],
1204 |       "metadata": {
1205 |         "colab": {
1206 |           "base_uri": "https://localhost:8080/",
1207 |           "height": 300
1208 |         },
1209 |         "id": "LI2SxXxERb8E",
1210 |         "outputId": "cf34ed87-4b8b-4372-ca25-d98b4b3558ad"
1211 |       },
1212 |       "execution_count": 43,
1213 |       "outputs": [
1214 |         {
1215 |           "output_type": "execute_result",
1216 |           "data": {
1217 |             "text/plain": [
1218 |               "       Sepal_Length  Sepal_Width  Petal_Length  Petal_Width\n",
1219 |               "count    150.000000   150.000000    150.000000   150.000000\n",
1220 |               "mean       5.843333     3.054000      3.758667     1.198667\n",
1221 |               "std        0.828066     0.433594      1.764420     0.763161\n",
1222 |               "min        4.300000     2.000000      1.000000     0.100000\n",
1223 |               "25%        5.100000     2.800000      1.600000     0.300000\n",
1224 |               "50%        5.800000     3.000000      4.350000     1.300000\n",
1225 |               "75%        6.400000     3.300000      5.100000     1.800000\n",
1226 |               "max        7.900000     4.400000      6.900000     2.500000"
1227 |             ],
1228 |             "text/html": [
1229 |               "\n",
1230 |               "  <div id=\"df-e212aeba-f401-48b1-9fbc-74c9063a20b5\">\n",
1231 |               "    <div class=\"colab-df-container\">\n",
1232 |               "      <div>\n",
1233 |               "<style scoped>\n",
1234 |               "    .dataframe tbody tr th:only-of-type {\n",
1235 |               "        vertical-align: middle;\n",
1236 |               "    }\n",
1237 |               "\n",
1238 |               "    .dataframe tbody tr th {\n",
1239 |               "        vertical-align: top;\n",
1240 |               "    }\n",
1241 |               "\n",
1242 |               "    .dataframe thead th {\n",
1243 |               "        text-align: right;\n",
1244 |               "    }\n",
1245 |               "</style>\n",
1246 |               "<table border=\"1\" class=\"dataframe\">\n",
1247 |               "  <thead>\n",
1248 |               "    <tr style=\"text-align: right;\">\n",
1249 |               "      <th></th>\n",
1250 |               "      <th>Sepal_Length</th>\n",
1251 |               "      <th>Sepal_Width</th>\n",
1252 |               "      <th>Petal_Length</th>\n",
1253 |               "      <th>Petal_Width</th>\n",
1254 |               "    </tr>\n",
1255 |               "  </thead>\n",
1256 |               "  <tbody>\n",
1257 |               "    <tr>\n",
1258 |               "      <th>count</th>\n",
1259 |               "      <td>150.000000</td>\n",
1260 |               "      <td>150.000000</td>\n",
1261 |               "      <td>150.000000</td>\n",
1262 |               "      <td>150.000000</td>\n",
1263 |               "    </tr>\n",
1264 |               "    <tr>\n",
1265 |               "      <th>mean</th>\n",
1266 |               "      <td>5.843333</td>\n",
1267 |               "      <td>3.054000</td>\n",
1268 |               "      <td>3.758667</td>\n",
1269 |               "      <td>1.198667</td>\n",
1270 |               "    </tr>\n",
1271 |               "    <tr>\n",
1272 |               "      <th>std</th>\n",
1273 |               "      <td>0.828066</td>\n",
1274 |               "      <td>0.433594</td>\n",
1275 |               "      <td>1.764420</td>\n",
1276 |               "      <td>0.763161</td>\n",
1277 |               "    </tr>\n",
1278 |               "    <tr>\n",
1279 |               "      <th>min</th>\n",
1280 |               "      <td>4.300000</td>\n",
1281 |               "      <td>2.000000</td>\n",
1282 |               "      <td>1.000000</td>\n",
1283 |               "      <td>0.100000</td>\n",
1284 |               "    </tr>\n",
1285 |               "    <tr>\n",
1286 |               "      <th>25%</th>\n",
1287 |               "      <td>5.100000</td>\n",
1288 |               "      <td>2.800000</td>\n",
1289 |               "      <td>1.600000</td>\n",
1290 |               "      <td>0.300000</td>\n",
1291 |               "    </tr>\n",
1292 |               "    <tr>\n",
1293 |               "      <th>50%</th>\n",
1294 |               "      <td>5.800000</td>\n",
1295 |               "      <td>3.000000</td>\n",
1296 |               "      <td>4.350000</td>\n",
1297 |               "      <td>1.300000</td>\n",
1298 |               "    </tr>\n",
1299 |               "    <tr>\n",
1300 |               "      <th>75%</th>\n",
1301 |               "      <td>6.400000</td>\n",
1302 |               "      <td>3.300000</td>\n",
1303 |               "      <td>5.100000</td>\n",
1304 |               "      <td>1.800000</td>\n",
1305 |               "    </tr>\n",
1306 |               "    <tr>\n",
1307 |               "      <th>max</th>\n",
1308 |               "      <td>7.900000</td>\n",
1309 |               "      <td>4.400000</td>\n",
1310 |               "      <td>6.900000</td>\n",
1311 |               "      <td>2.500000</td>\n",
1312 |               "    </tr>\n",
1313 |               "  </tbody>\n",
1314 |               "</table>\n",
1315 |               "</div>\n",
1316 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e212aeba-f401-48b1-9fbc-74c9063a20b5')\"\n",
1317 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
1318 |               "              style=\"display:none;\">\n",
1319 |               "        \n",
1320 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1321 |               "       width=\"24px\">\n",
1322 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
1323 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
1324 |               "  </svg>\n",
1325 |               "      </button>\n",
1326 |               "      \n",
1327 |               "  <style>\n",
1328 |               "    .colab-df-container {\n",
1329 |               "      display:flex;\n",
1330 |               "      flex-wrap:wrap;\n",
1331 |               "      gap: 12px;\n",
1332 |               "    }\n",
1333 |               "\n",
1334 |               "    .colab-df-convert {\n",
1335 |               "      background-color: #E8F0FE;\n",
1336 |               "      border: none;\n",
1337 |               "      border-radius: 50%;\n",
1338 |               "      cursor: pointer;\n",
1339 |               "      display: none;\n",
1340 |               "      fill: #1967D2;\n",
1341 |               "      height: 32px;\n",
1342 |               "      padding: 0 0 0 0;\n",
1343 |               "      width: 32px;\n",
1344 |               "    }\n",
1345 |               "\n",
1346 |               "    .colab-df-convert:hover {\n",
1347 |               "      background-color: #E2EBFA;\n",
1348 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1349 |               "      fill: #174EA6;\n",
1350 |               "    }\n",
1351 |               "\n",
1352 |               "    [theme=dark] .colab-df-convert {\n",
1353 |               "      background-color: #3B4455;\n",
1354 |               "      fill: #D2E3FC;\n",
1355 |               "    }\n",
1356 |               "\n",
1357 |               "    [theme=dark] .colab-df-convert:hover {\n",
1358 |               "      background-color: #434B5C;\n",
1359 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1360 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1361 |               "      fill: #FFFFFF;\n",
1362 |               "    }\n",
1363 |               "  </style>\n",
1364 |               "\n",
1365 |               "      <script>\n",
1366 |               "        const buttonEl =\n",
1367 |               "          document.querySelector('#df-e212aeba-f401-48b1-9fbc-74c9063a20b5 button.colab-df-convert');\n",
1368 |               "        buttonEl.style.display =\n",
1369 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1370 |               "\n",
1371 |               "        async function convertToInteractive(key) {\n",
1372 |               "          const element = document.querySelector('#df-e212aeba-f401-48b1-9fbc-74c9063a20b5');\n",
1373 |               "          const dataTable =\n",
1374 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1375 |               "                                                     [key], {});\n",
1376 |               "          if (!dataTable) return;\n",
1377 |               "\n",
1378 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1379 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1380 |               "            + ' to learn more about interactive tables.';\n",
1381 |               "          element.innerHTML = '';\n",
1382 |               "          dataTable['output_type'] = 'display_data';\n",
1383 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
1384 |               "          const docLink = document.createElement('div');\n",
1385 |               "          docLink.innerHTML = docLinkHtml;\n",
1386 |               "          element.appendChild(docLink);\n",
1387 |               "        }\n",
1388 |               "      </script>\n",
1389 |               "    </div>\n",
1390 |               "  </div>\n",
1391 |               "  "
1392 |             ]
1393 |           },
1394 |           "metadata": {},
1395 |           "execution_count": 43
1396 |         }
1397 |       ]
1398 |     },
1399 |     {
1400 |       "cell_type": "code",
1401 |       "source": [
1402 |         "iris.columns.values"
1403 |       ],
1404 |       "metadata": {
1405 |         "colab": {
1406 |           "base_uri": "https://localhost:8080/"
1407 |         },
1408 |         "id": "nZsEFbl5Rft6",
1409 |         "outputId": "b0091b8a-d449-4cf6-e883-393dd69d8e3a"
1410 |       },
1411 |       "execution_count": 44,
1412 |       "outputs": [
1413 |         {
1414 |           "output_type": "execute_result",
1415 |           "data": {
1416 |             "text/plain": [
1417 |               "array(['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width',\n",
1418 |               "       'Species'], dtype=object)"
1419 |             ]
1420 |           },
1421 |           "metadata": {},
1422 |           "execution_count": 44
1423 |         }
1424 |       ]
1425 |     },
1426 |     {
1427 |       "cell_type": "code",
1428 |       "source": [
1429 |         "iris.iloc[5]"
1430 |       ],
1431 |       "metadata": {
1432 |         "colab": {
1433 |           "base_uri": "https://localhost:8080/"
1434 |         },
1435 |         "id": "n5SX_lRzRkKZ",
1436 |         "outputId": "f379c9cd-bca8-4836-c1dd-3d9df7cbb29f"
1437 |       },
1438 |       "execution_count": 45,
1439 |       "outputs": [
1440 |         {
1441 |           "output_type": "execute_result",
1442 |           "data": {
1443 |             "text/plain": [
1444 |               "Sepal_Length            5.4\n",
1445 |               "Sepal_Width             3.9\n",
1446 |               "Petal_Length            1.7\n",
1447 |               "Petal_Width             0.4\n",
1448 |               "Species         Iris-setosa\n",
1449 |               "Name: 5, dtype: object"
1450 |             ]
1451 |           },
1452 |           "metadata": {},
1453 |           "execution_count": 45
1454 |         }
1455 |       ]
1456 |     },
1457 |     {
1458 |       "cell_type": "code",
1459 |       "source": [
1460 |         "iris[47:51]"
1461 |       ],
1462 |       "metadata": {
1463 |         "colab": {
1464 |           "base_uri": "https://localhost:8080/",
1465 |           "height": 175
1466 |         },
1467 |         "id": "iEZgPQiGRnpf",
1468 |         "outputId": "42f99d8a-0060-4464-cb88-9982f89d1b33"
1469 |       },
1470 |       "execution_count": 46,
1471 |       "outputs": [
1472 |         {
1473 |           "output_type": "execute_result",
1474 |           "data": {
1475 |             "text/plain": [
1476 |               "    Sepal_Length  Sepal_Width  Petal_Length  Petal_Width          Species\n",
1477 |               "47           4.6          3.2           1.4          0.2      Iris-setosa\n",
1478 |               "48           5.3          3.7           1.5          0.2      Iris-setosa\n",
1479 |               "49           5.0          3.3           1.4          0.2      Iris-setosa\n",
1480 |               "50           7.0          3.2           4.7          1.4  Iris-versicolor"
1481 |             ],
1482 |             "text/html": [
1483 |               "\n",
1484 |               "  <div id=\"df-361dbdc0-6da0-46b7-b7d6-9ac80c5f4462\">\n",
1485 |               "    <div class=\"colab-df-container\">\n",
1486 |               "      <div>\n",
1487 |               "<style scoped>\n",
1488 |               "    .dataframe tbody tr th:only-of-type {\n",
1489 |               "        vertical-align: middle;\n",
1490 |               "    }\n",
1491 |               "\n",
1492 |               "    .dataframe tbody tr th {\n",
1493 |               "        vertical-align: top;\n",
1494 |               "    }\n",
1495 |               "\n",
1496 |               "    .dataframe thead th {\n",
1497 |               "        text-align: right;\n",
1498 |               "    }\n",
1499 |               "</style>\n",
1500 |               "<table border=\"1\" class=\"dataframe\">\n",
1501 |               "  <thead>\n",
1502 |               "    <tr style=\"text-align: right;\">\n",
1503 |               "      <th></th>\n",
1504 |               "      <th>Sepal_Length</th>\n",
1505 |               "      <th>Sepal_Width</th>\n",
1506 |               "      <th>Petal_Length</th>\n",
1507 |               "      <th>Petal_Width</th>\n",
1508 |               "      <th>Species</th>\n",
1509 |               "    </tr>\n",
1510 |               "  </thead>\n",
1511 |               "  <tbody>\n",
1512 |               "    <tr>\n",
1513 |               "      <th>47</th>\n",
1514 |               "      <td>4.6</td>\n",
1515 |               "      <td>3.2</td>\n",
1516 |               "      <td>1.4</td>\n",
1517 |               "      <td>0.2</td>\n",
1518 |               "      <td>Iris-setosa</td>\n",
1519 |               "    </tr>\n",
1520 |               "    <tr>\n",
1521 |               "      <th>48</th>\n",
1522 |               "      <td>5.3</td>\n",
1523 |               "      <td>3.7</td>\n",
1524 |               "      <td>1.5</td>\n",
1525 |               "      <td>0.2</td>\n",
1526 |               "      <td>Iris-setosa</td>\n",
1527 |               "    </tr>\n",
1528 |               "    <tr>\n",
1529 |               "      <th>49</th>\n",
1530 |               "      <td>5.0</td>\n",
1531 |               "      <td>3.3</td>\n",
1532 |               "      <td>1.4</td>\n",
1533 |               "      <td>0.2</td>\n",
1534 |               "      <td>Iris-setosa</td>\n",
1535 |               "    </tr>\n",
1536 |               "    <tr>\n",
1537 |               "      <th>50</th>\n",
1538 |               "      <td>7.0</td>\n",
1539 |               "      <td>3.2</td>\n",
1540 |               "      <td>4.7</td>\n",
1541 |               "      <td>1.4</td>\n",
1542 |               "      <td>Iris-versicolor</td>\n",
1543 |               "    </tr>\n",
1544 |               "  </tbody>\n",
1545 |               "</table>\n",
1546 |               "</div>\n",
1547 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-361dbdc0-6da0-46b7-b7d6-9ac80c5f4462')\"\n",
1548 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
1549 |               "              style=\"display:none;\">\n",
1550 |               "        \n",
1551 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1552 |               "       width=\"24px\">\n",
1553 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
1554 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
1555 |               "  </svg>\n",
1556 |               "      </button>\n",
1557 |               "      \n",
1558 |               "  <style>\n",
1559 |               "    .colab-df-container {\n",
1560 |               "      display:flex;\n",
1561 |               "      flex-wrap:wrap;\n",
1562 |               "      gap: 12px;\n",
1563 |               "    }\n",
1564 |               "\n",
1565 |               "    .colab-df-convert {\n",
1566 |               "      background-color: #E8F0FE;\n",
1567 |               "      border: none;\n",
1568 |               "      border-radius: 50%;\n",
1569 |               "      cursor: pointer;\n",
1570 |               "      display: none;\n",
1571 |               "      fill: #1967D2;\n",
1572 |               "      height: 32px;\n",
1573 |               "      padding: 0 0 0 0;\n",
1574 |               "      width: 32px;\n",
1575 |               "    }\n",
1576 |               "\n",
1577 |               "    .colab-df-convert:hover {\n",
1578 |               "      background-color: #E2EBFA;\n",
1579 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1580 |               "      fill: #174EA6;\n",
1581 |               "    }\n",
1582 |               "\n",
1583 |               "    [theme=dark] .colab-df-convert {\n",
1584 |               "      background-color: #3B4455;\n",
1585 |               "      fill: #D2E3FC;\n",
1586 |               "    }\n",
1587 |               "\n",
1588 |               "    [theme=dark] .colab-df-convert:hover {\n",
1589 |               "      background-color: #434B5C;\n",
1590 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1591 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1592 |               "      fill: #FFFFFF;\n",
1593 |               "    }\n",
1594 |               "  </style>\n",
1595 |               "\n",
1596 |               "      <script>\n",
1597 |               "        const buttonEl =\n",
1598 |               "          document.querySelector('#df-361dbdc0-6da0-46b7-b7d6-9ac80c5f4462 button.colab-df-convert');\n",
1599 |               "        buttonEl.style.display =\n",
1600 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1601 |               "\n",
1602 |               "        async function convertToInteractive(key) {\n",
1603 |               "          const element = document.querySelector('#df-361dbdc0-6da0-46b7-b7d6-9ac80c5f4462');\n",
1604 |               "          const dataTable =\n",
1605 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1606 |               "                                                     [key], {});\n",
1607 |               "          if (!dataTable) return;\n",
1608 |               "\n",
1609 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1610 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1611 |               "            + ' to learn more about interactive tables.';\n",
1612 |               "          element.innerHTML = '';\n",
1613 |               "          dataTable['output_type'] = 'display_data';\n",
1614 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
1615 |               "          const docLink = document.createElement('div');\n",
1616 |               "          docLink.innerHTML = docLinkHtml;\n",
1617 |               "          element.appendChild(docLink);\n",
1618 |               "        }\n",
1619 |               "      </script>\n",
1620 |               "    </div>\n",
1621 |               "  </div>\n",
1622 |               "  "
1623 |             ]
1624 |           },
1625 |           "metadata": {},
1626 |           "execution_count": 46
1627 |         }
1628 |       ]
1629 |     },
1630 |     {
1631 |       "cell_type": "code",
1632 |       "source": [
1633 |         "iris.loc[:,[\"Sepal_Length\",\"Sepal_Width\"]] #See data column wise"
1634 |       ],
1635 |       "metadata": {
1636 |         "colab": {
1637 |           "base_uri": "https://localhost:8080/",
1638 |           "height": 424
1639 |         },
1640 |         "id": "6-kbFJ_nRq8f",
1641 |         "outputId": "c974f457-c0b1-498b-c76c-3c32f044c574"
1642 |       },
1643 |       "execution_count": 47,
1644 |       "outputs": [
1645 |         {
1646 |           "output_type": "execute_result",
1647 |           "data": {
1648 |             "text/plain": [
1649 |               "     Sepal_Length  Sepal_Width\n",
1650 |               "0             5.1          3.5\n",
1651 |               "1             4.9          3.0\n",
1652 |               "2             4.7          3.2\n",
1653 |               "3             4.6          3.1\n",
1654 |               "4             5.0          3.6\n",
1655 |               "..            ...          ...\n",
1656 |               "145           6.7          3.0\n",
1657 |               "146           6.3          2.5\n",
1658 |               "147           6.5          3.0\n",
1659 |               "148           6.2          3.4\n",
1660 |               "149           5.9          3.0\n",
1661 |               "\n",
1662 |               "[150 rows x 2 columns]"
1663 |             ],
1664 |             "text/html": [
1665 |               "\n",
1666 |               "  <div id=\"df-599e8700-6bb2-4cf3-a766-db021c421148\">\n",
1667 |               "    <div class=\"colab-df-container\">\n",
1668 |               "      <div>\n",
1669 |               "<style scoped>\n",
1670 |               "    .dataframe tbody tr th:only-of-type {\n",
1671 |               "        vertical-align: middle;\n",
1672 |               "    }\n",
1673 |               "\n",
1674 |               "    .dataframe tbody tr th {\n",
1675 |               "        vertical-align: top;\n",
1676 |               "    }\n",
1677 |               "\n",
1678 |               "    .dataframe thead th {\n",
1679 |               "        text-align: right;\n",
1680 |               "    }\n",
1681 |               "</style>\n",
1682 |               "<table border=\"1\" class=\"dataframe\">\n",
1683 |               "  <thead>\n",
1684 |               "    <tr style=\"text-align: right;\">\n",
1685 |               "      <th></th>\n",
1686 |               "      <th>Sepal_Length</th>\n",
1687 |               "      <th>Sepal_Width</th>\n",
1688 |               "    </tr>\n",
1689 |               "  </thead>\n",
1690 |               "  <tbody>\n",
1691 |               "    <tr>\n",
1692 |               "      <th>0</th>\n",
1693 |               "      <td>5.1</td>\n",
1694 |               "      <td>3.5</td>\n",
1695 |               "    </tr>\n",
1696 |               "    <tr>\n",
1697 |               "      <th>1</th>\n",
1698 |               "      <td>4.9</td>\n",
1699 |               "      <td>3.0</td>\n",
1700 |               "    </tr>\n",
1701 |               "    <tr>\n",
1702 |               "      <th>2</th>\n",
1703 |               "      <td>4.7</td>\n",
1704 |               "      <td>3.2</td>\n",
1705 |               "    </tr>\n",
1706 |               "    <tr>\n",
1707 |               "      <th>3</th>\n",
1708 |               "      <td>4.6</td>\n",
1709 |               "      <td>3.1</td>\n",
1710 |               "    </tr>\n",
1711 |               "    <tr>\n",
1712 |               "      <th>4</th>\n",
1713 |               "      <td>5.0</td>\n",
1714 |               "      <td>3.6</td>\n",
1715 |               "    </tr>\n",
1716 |               "    <tr>\n",
1717 |               "      <th>...</th>\n",
1718 |               "      <td>...</td>\n",
1719 |               "      <td>...</td>\n",
1720 |               "    </tr>\n",
1721 |               "    <tr>\n",
1722 |               "      <th>145</th>\n",
1723 |               "      <td>6.7</td>\n",
1724 |               "      <td>3.0</td>\n",
1725 |               "    </tr>\n",
1726 |               "    <tr>\n",
1727 |               "      <th>146</th>\n",
1728 |               "      <td>6.3</td>\n",
1729 |               "      <td>2.5</td>\n",
1730 |               "    </tr>\n",
1731 |               "    <tr>\n",
1732 |               "      <th>147</th>\n",
1733 |               "      <td>6.5</td>\n",
1734 |               "      <td>3.0</td>\n",
1735 |               "    </tr>\n",
1736 |               "    <tr>\n",
1737 |               "      <th>148</th>\n",
1738 |               "      <td>6.2</td>\n",
1739 |               "      <td>3.4</td>\n",
1740 |               "    </tr>\n",
1741 |               "    <tr>\n",
1742 |               "      <th>149</th>\n",
1743 |               "      <td>5.9</td>\n",
1744 |               "      <td>3.0</td>\n",
1745 |               "    </tr>\n",
1746 |               "  </tbody>\n",
1747 |               "</table>\n",
1748 |               "<p>150 rows × 2 columns</p>\n",
1749 |               "</div>\n",
1750 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-599e8700-6bb2-4cf3-a766-db021c421148')\"\n",
1751 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
1752 |               "              style=\"display:none;\">\n",
1753 |               "        \n",
1754 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1755 |               "       width=\"24px\">\n",
1756 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
1757 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
1758 |               "  </svg>\n",
1759 |               "      </button>\n",
1760 |               "      \n",
1761 |               "  <style>\n",
1762 |               "    .colab-df-container {\n",
1763 |               "      display:flex;\n",
1764 |               "      flex-wrap:wrap;\n",
1765 |               "      gap: 12px;\n",
1766 |               "    }\n",
1767 |               "\n",
1768 |               "    .colab-df-convert {\n",
1769 |               "      background-color: #E8F0FE;\n",
1770 |               "      border: none;\n",
1771 |               "      border-radius: 50%;\n",
1772 |               "      cursor: pointer;\n",
1773 |               "      display: none;\n",
1774 |               "      fill: #1967D2;\n",
1775 |               "      height: 32px;\n",
1776 |               "      padding: 0 0 0 0;\n",
1777 |               "      width: 32px;\n",
1778 |               "    }\n",
1779 |               "\n",
1780 |               "    .colab-df-convert:hover {\n",
1781 |               "      background-color: #E2EBFA;\n",
1782 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1783 |               "      fill: #174EA6;\n",
1784 |               "    }\n",
1785 |               "\n",
1786 |               "    [theme=dark] .colab-df-convert {\n",
1787 |               "      background-color: #3B4455;\n",
1788 |               "      fill: #D2E3FC;\n",
1789 |               "    }\n",
1790 |               "\n",
1791 |               "    [theme=dark] .colab-df-convert:hover {\n",
1792 |               "      background-color: #434B5C;\n",
1793 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1794 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1795 |               "      fill: #FFFFFF;\n",
1796 |               "    }\n",
1797 |               "  </style>\n",
1798 |               "\n",
1799 |               "      <script>\n",
1800 |               "        const buttonEl =\n",
1801 |               "          document.querySelector('#df-599e8700-6bb2-4cf3-a766-db021c421148 button.colab-df-convert');\n",
1802 |               "        buttonEl.style.display =\n",
1803 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1804 |               "\n",
1805 |               "        async function convertToInteractive(key) {\n",
1806 |               "          const element = document.querySelector('#df-599e8700-6bb2-4cf3-a766-db021c421148');\n",
1807 |               "          const dataTable =\n",
1808 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1809 |               "                                                     [key], {});\n",
1810 |               "          if (!dataTable) return;\n",
1811 |               "\n",
1812 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1813 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1814 |               "            + ' to learn more about interactive tables.';\n",
1815 |               "          element.innerHTML = '';\n",
1816 |               "          dataTable['output_type'] = 'display_data';\n",
1817 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
1818 |               "          const docLink = document.createElement('div');\n",
1819 |               "          docLink.innerHTML = docLinkHtml;\n",
1820 |               "          element.appendChild(docLink);\n",
1821 |               "        }\n",
1822 |               "      </script>\n",
1823 |               "    </div>\n",
1824 |               "  </div>\n",
1825 |               "  "
1826 |             ]
1827 |           },
1828 |           "metadata": {},
1829 |           "execution_count": 47
1830 |         }
1831 |       ]
1832 |     },
1833 |     {
1834 |       "cell_type": "code",
1835 |       "source": [
1836 |         "cols_2_4=iris.columns[2:4]\n",
1837 |         "iris[cols_2_4]\n",
1838 |         "     "
1839 |       ],
1840 |       "metadata": {
1841 |         "colab": {
1842 |           "base_uri": "https://localhost:8080/",
1843 |           "height": 424
1844 |         },
1845 |         "id": "ekMGhHz7Rt5e",
1846 |         "outputId": "4a1188aa-2ce3-4c07-acab-f25807e46c07"
1847 |       },
1848 |       "execution_count": 48,
1849 |       "outputs": [
1850 |         {
1851 |           "output_type": "execute_result",
1852 |           "data": {
1853 |             "text/plain": [
1854 |               "     Petal_Length  Petal_Width\n",
1855 |               "0             1.4          0.2\n",
1856 |               "1             1.4          0.2\n",
1857 |               "2             1.3          0.2\n",
1858 |               "3             1.5          0.2\n",
1859 |               "4             1.4          0.2\n",
1860 |               "..            ...          ...\n",
1861 |               "145           5.2          2.3\n",
1862 |               "146           5.0          1.9\n",
1863 |               "147           5.2          2.0\n",
1864 |               "148           5.4          2.3\n",
1865 |               "149           5.1          1.8\n",
1866 |               "\n",
1867 |               "[150 rows x 2 columns]"
1868 |             ],
1869 |             "text/html": [
1870 |               "\n",
1871 |               "  <div id=\"df-066faa7a-7c43-4445-8260-48fcd83f4a4a\">\n",
1872 |               "    <div class=\"colab-df-container\">\n",
1873 |               "      <div>\n",
1874 |               "<style scoped>\n",
1875 |               "    .dataframe tbody tr th:only-of-type {\n",
1876 |               "        vertical-align: middle;\n",
1877 |               "    }\n",
1878 |               "\n",
1879 |               "    .dataframe tbody tr th {\n",
1880 |               "        vertical-align: top;\n",
1881 |               "    }\n",
1882 |               "\n",
1883 |               "    .dataframe thead th {\n",
1884 |               "        text-align: right;\n",
1885 |               "    }\n",
1886 |               "</style>\n",
1887 |               "<table border=\"1\" class=\"dataframe\">\n",
1888 |               "  <thead>\n",
1889 |               "    <tr style=\"text-align: right;\">\n",
1890 |               "      <th></th>\n",
1891 |               "      <th>Petal_Length</th>\n",
1892 |               "      <th>Petal_Width</th>\n",
1893 |               "    </tr>\n",
1894 |               "  </thead>\n",
1895 |               "  <tbody>\n",
1896 |               "    <tr>\n",
1897 |               "      <th>0</th>\n",
1898 |               "      <td>1.4</td>\n",
1899 |               "      <td>0.2</td>\n",
1900 |               "    </tr>\n",
1901 |               "    <tr>\n",
1902 |               "      <th>1</th>\n",
1903 |               "      <td>1.4</td>\n",
1904 |               "      <td>0.2</td>\n",
1905 |               "    </tr>\n",
1906 |               "    <tr>\n",
1907 |               "      <th>2</th>\n",
1908 |               "      <td>1.3</td>\n",
1909 |               "      <td>0.2</td>\n",
1910 |               "    </tr>\n",
1911 |               "    <tr>\n",
1912 |               "      <th>3</th>\n",
1913 |               "      <td>1.5</td>\n",
1914 |               "      <td>0.2</td>\n",
1915 |               "    </tr>\n",
1916 |               "    <tr>\n",
1917 |               "      <th>4</th>\n",
1918 |               "      <td>1.4</td>\n",
1919 |               "      <td>0.2</td>\n",
1920 |               "    </tr>\n",
1921 |               "    <tr>\n",
1922 |               "      <th>...</th>\n",
1923 |               "      <td>...</td>\n",
1924 |               "      <td>...</td>\n",
1925 |               "    </tr>\n",
1926 |               "    <tr>\n",
1927 |               "      <th>145</th>\n",
1928 |               "      <td>5.2</td>\n",
1929 |               "      <td>2.3</td>\n",
1930 |               "    </tr>\n",
1931 |               "    <tr>\n",
1932 |               "      <th>146</th>\n",
1933 |               "      <td>5.0</td>\n",
1934 |               "      <td>1.9</td>\n",
1935 |               "    </tr>\n",
1936 |               "    <tr>\n",
1937 |               "      <th>147</th>\n",
1938 |               "      <td>5.2</td>\n",
1939 |               "      <td>2.0</td>\n",
1940 |               "    </tr>\n",
1941 |               "    <tr>\n",
1942 |               "      <th>148</th>\n",
1943 |               "      <td>5.4</td>\n",
1944 |               "      <td>2.3</td>\n",
1945 |               "    </tr>\n",
1946 |               "    <tr>\n",
1947 |               "      <th>149</th>\n",
1948 |               "      <td>5.1</td>\n",
1949 |               "      <td>1.8</td>\n",
1950 |               "    </tr>\n",
1951 |               "  </tbody>\n",
1952 |               "</table>\n",
1953 |               "<p>150 rows × 2 columns</p>\n",
1954 |               "</div>\n",
1955 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-066faa7a-7c43-4445-8260-48fcd83f4a4a')\"\n",
1956 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
1957 |               "              style=\"display:none;\">\n",
1958 |               "        \n",
1959 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1960 |               "       width=\"24px\">\n",
1961 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
1962 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
1963 |               "  </svg>\n",
1964 |               "      </button>\n",
1965 |               "      \n",
1966 |               "  <style>\n",
1967 |               "    .colab-df-container {\n",
1968 |               "      display:flex;\n",
1969 |               "      flex-wrap:wrap;\n",
1970 |               "      gap: 12px;\n",
1971 |               "    }\n",
1972 |               "\n",
1973 |               "    .colab-df-convert {\n",
1974 |               "      background-color: #E8F0FE;\n",
1975 |               "      border: none;\n",
1976 |               "      border-radius: 50%;\n",
1977 |               "      cursor: pointer;\n",
1978 |               "      display: none;\n",
1979 |               "      fill: #1967D2;\n",
1980 |               "      height: 32px;\n",
1981 |               "      padding: 0 0 0 0;\n",
1982 |               "      width: 32px;\n",
1983 |               "    }\n",
1984 |               "\n",
1985 |               "    .colab-df-convert:hover {\n",
1986 |               "      background-color: #E2EBFA;\n",
1987 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1988 |               "      fill: #174EA6;\n",
1989 |               "    }\n",
1990 |               "\n",
1991 |               "    [theme=dark] .colab-df-convert {\n",
1992 |               "      background-color: #3B4455;\n",
1993 |               "      fill: #D2E3FC;\n",
1994 |               "    }\n",
1995 |               "\n",
1996 |               "    [theme=dark] .colab-df-convert:hover {\n",
1997 |               "      background-color: #434B5C;\n",
1998 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1999 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
2000 |               "      fill: #FFFFFF;\n",
2001 |               "    }\n",
2002 |               "  </style>\n",
2003 |               "\n",
2004 |               "      <script>\n",
2005 |               "        const buttonEl =\n",
2006 |               "          document.querySelector('#df-066faa7a-7c43-4445-8260-48fcd83f4a4a button.colab-df-convert');\n",
2007 |               "        buttonEl.style.display =\n",
2008 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
2009 |               "\n",
2010 |               "        async function convertToInteractive(key) {\n",
2011 |               "          const element = document.querySelector('#df-066faa7a-7c43-4445-8260-48fcd83f4a4a');\n",
2012 |               "          const dataTable =\n",
2013 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
2014 |               "                                                     [key], {});\n",
2015 |               "          if (!dataTable) return;\n",
2016 |               "\n",
2017 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
2018 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
2019 |               "            + ' to learn more about interactive tables.';\n",
2020 |               "          element.innerHTML = '';\n",
2021 |               "          dataTable['output_type'] = 'display_data';\n",
2022 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
2023 |               "          const docLink = document.createElement('div');\n",
2024 |               "          docLink.innerHTML = docLinkHtml;\n",
2025 |               "          element.appendChild(docLink);\n",
2026 |               "        }\n",
2027 |               "      </script>\n",
2028 |               "    </div>\n",
2029 |               "  </div>\n",
2030 |               "  "
2031 |             ]
2032 |           },
2033 |           "metadata": {},
2034 |           "execution_count": 48
2035 |         }
2036 |       ]
2037 |     },
2038 |     {
2039 |       "cell_type": "code",
2040 |       "source": [
2041 |         "iris.isnull().any()"
2042 |       ],
2043 |       "metadata": {
2044 |         "colab": {
2045 |           "base_uri": "https://localhost:8080/"
2046 |         },
2047 |         "id": "X6iSrfeVRxS5",
2048 |         "outputId": "e020a857-5d45-4e33-ea13-7b6891801bbb"
2049 |       },
2050 |       "execution_count": 49,
2051 |       "outputs": [
2052 |         {
2053 |           "output_type": "execute_result",
2054 |           "data": {
2055 |             "text/plain": [
2056 |               "Sepal_Length    False\n",
2057 |               "Sepal_Width     False\n",
2058 |               "Petal_Length    False\n",
2059 |               "Petal_Width     False\n",
2060 |               "Species         False\n",
2061 |               "dtype: bool"
2062 |             ]
2063 |           },
2064 |           "metadata": {},
2065 |           "execution_count": 49
2066 |         }
2067 |       ]
2068 |     },
2069 |     {
2070 |       "cell_type": "code",
2071 |       "source": [
2072 |         "iris.isnull().sum()"
2073 |       ],
2074 |       "metadata": {
2075 |         "colab": {
2076 |           "base_uri": "https://localhost:8080/"
2077 |         },
2078 |         "id": "TieDzHG1Rz-2",
2079 |         "outputId": "bfbb27a7-6b51-4058-971b-15cf48896a2e"
2080 |       },
2081 |       "execution_count": 50,
2082 |       "outputs": [
2083 |         {
2084 |           "output_type": "execute_result",
2085 |           "data": {
2086 |             "text/plain": [
2087 |               "Sepal_Length    0\n",
2088 |               "Sepal_Width     0\n",
2089 |               "Petal_Length    0\n",
2090 |               "Petal_Width     0\n",
2091 |               "Species         0\n",
2092 |               "dtype: int64"
2093 |             ]
2094 |           },
2095 |           "metadata": {},
2096 |           "execution_count": 50
2097 |         }
2098 |       ]
2099 |     },
2100 |     {
2101 |       "cell_type": "code",
2102 |       "source": [
2103 |         "iris.dtypes"
2104 |       ],
2105 |       "metadata": {
2106 |         "colab": {
2107 |           "base_uri": "https://localhost:8080/"
2108 |         },
2109 |         "id": "Hifc2iyQR25-",
2110 |         "outputId": "2fafd84f-4f15-411b-a5f5-f62dbb6a7ff9"
2111 |       },
2112 |       "execution_count": 51,
2113 |       "outputs": [
2114 |         {
2115 |           "output_type": "execute_result",
2116 |           "data": {
2117 |             "text/plain": [
2118 |               "Sepal_Length    float64\n",
2119 |               "Sepal_Width     float64\n",
2120 |               "Petal_Length    float64\n",
2121 |               "Petal_Width     float64\n",
2122 |               "Species          object\n",
2123 |               "dtype: object"
2124 |             ]
2125 |           },
2126 |           "metadata": {},
2127 |           "execution_count": 51
2128 |         }
2129 |       ]
2130 |     },
2131 |     {
2132 |       "cell_type": "code",
2133 |       "source": [
2134 |         "df=iris\n",
2135 |         "df['petal Length(cm)']=iris['Petal_Length'].astype(\"int\")"
2136 |       ],
2137 |       "metadata": {
2138 |         "id": "xOLC5WdOR5s8"
2139 |       },
2140 |       "execution_count": 52,
2141 |       "outputs": []
2142 |     },
2143 |     {
2144 |       "cell_type": "code",
2145 |       "source": [
2146 |         "df1=df"
2147 |       ],
2148 |       "metadata": {
2149 |         "id": "b1gsd_49R8vK"
2150 |       },
2151 |       "execution_count": 53,
2152 |       "outputs": []
2153 |     },
2154 |     {
2155 |       "cell_type": "code",
2156 |       "source": [
2157 |         "df"
2158 |       ],
2159 |       "metadata": {
2160 |         "colab": {
2161 |           "base_uri": "https://localhost:8080/",
2162 |           "height": 424
2163 |         },
2164 |         "id": "PT4v4WsuR_Iy",
2165 |         "outputId": "7800571c-c3d5-43a6-fab0-db0e6cd71ab8"
2166 |       },
2167 |       "execution_count": 54,
2168 |       "outputs": [
2169 |         {
2170 |           "output_type": "execute_result",
2171 |           "data": {
2172 |             "text/plain": [
2173 |               "     Sepal_Length  Sepal_Width  Petal_Length  Petal_Width         Species  \\\n",
2174 |               "0             5.1          3.5           1.4          0.2     Iris-setosa   \n",
2175 |               "1             4.9          3.0           1.4          0.2     Iris-setosa   \n",
2176 |               "2             4.7          3.2           1.3          0.2     Iris-setosa   \n",
2177 |               "3             4.6          3.1           1.5          0.2     Iris-setosa   \n",
2178 |               "4             5.0          3.6           1.4          0.2     Iris-setosa   \n",
2179 |               "..            ...          ...           ...          ...             ...   \n",
2180 |               "145           6.7          3.0           5.2          2.3  Iris-virginica   \n",
2181 |               "146           6.3          2.5           5.0          1.9  Iris-virginica   \n",
2182 |               "147           6.5          3.0           5.2          2.0  Iris-virginica   \n",
2183 |               "148           6.2          3.4           5.4          2.3  Iris-virginica   \n",
2184 |               "149           5.9          3.0           5.1          1.8  Iris-virginica   \n",
2185 |               "\n",
2186 |               "     petal Length(cm)  \n",
2187 |               "0                   1  \n",
2188 |               "1                   1  \n",
2189 |               "2                   1  \n",
2190 |               "3                   1  \n",
2191 |               "4                   1  \n",
2192 |               "..                ...  \n",
2193 |               "145                 5  \n",
2194 |               "146                 5  \n",
2195 |               "147                 5  \n",
2196 |               "148                 5  \n",
2197 |               "149                 5  \n",
2198 |               "\n",
2199 |               "[150 rows x 6 columns]"
2200 |             ],
2201 |             "text/html": [
2202 |               "\n",
2203 |               "  <div id=\"df-4c69c103-4fe9-477d-abe1-aca980d6fa19\">\n",
2204 |               "    <div class=\"colab-df-container\">\n",
2205 |               "      <div>\n",
2206 |               "<style scoped>\n",
2207 |               "    .dataframe tbody tr th:only-of-type {\n",
2208 |               "        vertical-align: middle;\n",
2209 |               "    }\n",
2210 |               "\n",
2211 |               "    .dataframe tbody tr th {\n",
2212 |               "        vertical-align: top;\n",
2213 |               "    }\n",
2214 |               "\n",
2215 |               "    .dataframe thead th {\n",
2216 |               "        text-align: right;\n",
2217 |               "    }\n",
2218 |               "</style>\n",
2219 |               "<table border=\"1\" class=\"dataframe\">\n",
2220 |               "  <thead>\n",
2221 |               "    <tr style=\"text-align: right;\">\n",
2222 |               "      <th></th>\n",
2223 |               "      <th>Sepal_Length</th>\n",
2224 |               "      <th>Sepal_Width</th>\n",
2225 |               "      <th>Petal_Length</th>\n",
2226 |               "      <th>Petal_Width</th>\n",
2227 |               "      <th>Species</th>\n",
2228 |               "      <th>petal Length(cm)</th>\n",
2229 |               "    </tr>\n",
2230 |               "  </thead>\n",
2231 |               "  <tbody>\n",
2232 |               "    <tr>\n",
2233 |               "      <th>0</th>\n",
2234 |               "      <td>5.1</td>\n",
2235 |               "      <td>3.5</td>\n",
2236 |               "      <td>1.4</td>\n",
2237 |               "      <td>0.2</td>\n",
2238 |               "      <td>Iris-setosa</td>\n",
2239 |               "      <td>1</td>\n",
2240 |               "    </tr>\n",
2241 |               "    <tr>\n",
2242 |               "      <th>1</th>\n",
2243 |               "      <td>4.9</td>\n",
2244 |               "      <td>3.0</td>\n",
2245 |               "      <td>1.4</td>\n",
2246 |               "      <td>0.2</td>\n",
2247 |               "      <td>Iris-setosa</td>\n",
2248 |               "      <td>1</td>\n",
2249 |               "    </tr>\n",
2250 |               "    <tr>\n",
2251 |               "      <th>2</th>\n",
2252 |               "      <td>4.7</td>\n",
2253 |               "      <td>3.2</td>\n",
2254 |               "      <td>1.3</td>\n",
2255 |               "      <td>0.2</td>\n",
2256 |               "      <td>Iris-setosa</td>\n",
2257 |               "      <td>1</td>\n",
2258 |               "    </tr>\n",
2259 |               "    <tr>\n",
2260 |               "      <th>3</th>\n",
2261 |               "      <td>4.6</td>\n",
2262 |               "      <td>3.1</td>\n",
2263 |               "      <td>1.5</td>\n",
2264 |               "      <td>0.2</td>\n",
2265 |               "      <td>Iris-setosa</td>\n",
2266 |               "      <td>1</td>\n",
2267 |               "    </tr>\n",
2268 |               "    <tr>\n",
2269 |               "      <th>4</th>\n",
2270 |               "      <td>5.0</td>\n",
2271 |               "      <td>3.6</td>\n",
2272 |               "      <td>1.4</td>\n",
2273 |               "      <td>0.2</td>\n",
2274 |               "      <td>Iris-setosa</td>\n",
2275 |               "      <td>1</td>\n",
2276 |               "    </tr>\n",
2277 |               "    <tr>\n",
2278 |               "      <th>...</th>\n",
2279 |               "      <td>...</td>\n",
2280 |               "      <td>...</td>\n",
2281 |               "      <td>...</td>\n",
2282 |               "      <td>...</td>\n",
2283 |               "      <td>...</td>\n",
2284 |               "      <td>...</td>\n",
2285 |               "    </tr>\n",
2286 |               "    <tr>\n",
2287 |               "      <th>145</th>\n",
2288 |               "      <td>6.7</td>\n",
2289 |               "      <td>3.0</td>\n",
2290 |               "      <td>5.2</td>\n",
2291 |               "      <td>2.3</td>\n",
2292 |               "      <td>Iris-virginica</td>\n",
2293 |               "      <td>5</td>\n",
2294 |               "    </tr>\n",
2295 |               "    <tr>\n",
2296 |               "      <th>146</th>\n",
2297 |               "      <td>6.3</td>\n",
2298 |               "      <td>2.5</td>\n",
2299 |               "      <td>5.0</td>\n",
2300 |               "      <td>1.9</td>\n",
2301 |               "      <td>Iris-virginica</td>\n",
2302 |               "      <td>5</td>\n",
2303 |               "    </tr>\n",
2304 |               "    <tr>\n",
2305 |               "      <th>147</th>\n",
2306 |               "      <td>6.5</td>\n",
2307 |               "      <td>3.0</td>\n",
2308 |               "      <td>5.2</td>\n",
2309 |               "      <td>2.0</td>\n",
2310 |               "      <td>Iris-virginica</td>\n",
2311 |               "      <td>5</td>\n",
2312 |               "    </tr>\n",
2313 |               "    <tr>\n",
2314 |               "      <th>148</th>\n",
2315 |               "      <td>6.2</td>\n",
2316 |               "      <td>3.4</td>\n",
2317 |               "      <td>5.4</td>\n",
2318 |               "      <td>2.3</td>\n",
2319 |               "      <td>Iris-virginica</td>\n",
2320 |               "      <td>5</td>\n",
2321 |               "    </tr>\n",
2322 |               "    <tr>\n",
2323 |               "      <th>149</th>\n",
2324 |               "      <td>5.9</td>\n",
2325 |               "      <td>3.0</td>\n",
2326 |               "      <td>5.1</td>\n",
2327 |               "      <td>1.8</td>\n",
2328 |               "      <td>Iris-virginica</td>\n",
2329 |               "      <td>5</td>\n",
2330 |               "    </tr>\n",
2331 |               "  </tbody>\n",
2332 |               "</table>\n",
2333 |               "<p>150 rows × 6 columns</p>\n",
2334 |               "</div>\n",
2335 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c69c103-4fe9-477d-abe1-aca980d6fa19')\"\n",
2336 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
2337 |               "              style=\"display:none;\">\n",
2338 |               "        \n",
2339 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
2340 |               "       width=\"24px\">\n",
2341 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
2342 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
2343 |               "  </svg>\n",
2344 |               "      </button>\n",
2345 |               "      \n",
2346 |               "  <style>\n",
2347 |               "    .colab-df-container {\n",
2348 |               "      display:flex;\n",
2349 |               "      flex-wrap:wrap;\n",
2350 |               "      gap: 12px;\n",
2351 |               "    }\n",
2352 |               "\n",
2353 |               "    .colab-df-convert {\n",
2354 |               "      background-color: #E8F0FE;\n",
2355 |               "      border: none;\n",
2356 |               "      border-radius: 50%;\n",
2357 |               "      cursor: pointer;\n",
2358 |               "      display: none;\n",
2359 |               "      fill: #1967D2;\n",
2360 |               "      height: 32px;\n",
2361 |               "      padding: 0 0 0 0;\n",
2362 |               "      width: 32px;\n",
2363 |               "    }\n",
2364 |               "\n",
2365 |               "    .colab-df-convert:hover {\n",
2366 |               "      background-color: #E2EBFA;\n",
2367 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
2368 |               "      fill: #174EA6;\n",
2369 |               "    }\n",
2370 |               "\n",
2371 |               "    [theme=dark] .colab-df-convert {\n",
2372 |               "      background-color: #3B4455;\n",
2373 |               "      fill: #D2E3FC;\n",
2374 |               "    }\n",
2375 |               "\n",
2376 |               "    [theme=dark] .colab-df-convert:hover {\n",
2377 |               "      background-color: #434B5C;\n",
2378 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
2379 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
2380 |               "      fill: #FFFFFF;\n",
2381 |               "    }\n",
2382 |               "  </style>\n",
2383 |               "\n",
2384 |               "      <script>\n",
2385 |               "        const buttonEl =\n",
2386 |               "          document.querySelector('#df-4c69c103-4fe9-477d-abe1-aca980d6fa19 button.colab-df-convert');\n",
2387 |               "        buttonEl.style.display =\n",
2388 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
2389 |               "\n",
2390 |               "        async function convertToInteractive(key) {\n",
2391 |               "          const element = document.querySelector('#df-4c69c103-4fe9-477d-abe1-aca980d6fa19');\n",
2392 |               "          const dataTable =\n",
2393 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
2394 |               "                                                     [key], {});\n",
2395 |               "          if (!dataTable) return;\n",
2396 |               "\n",
2397 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
2398 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
2399 |               "            + ' to learn more about interactive tables.';\n",
2400 |               "          element.innerHTML = '';\n",
2401 |               "          dataTable['output_type'] = 'display_data';\n",
2402 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
2403 |               "          const docLink = document.createElement('div');\n",
2404 |               "          docLink.innerHTML = docLinkHtml;\n",
2405 |               "          element.appendChild(docLink);\n",
2406 |               "        }\n",
2407 |               "      </script>\n",
2408 |               "    </div>\n",
2409 |               "  </div>\n",
2410 |               "  "
2411 |             ]
2412 |           },
2413 |           "metadata": {},
2414 |           "execution_count": 54
2415 |         }
2416 |       ]
2417 |     },
2418 |     {
2419 |       "cell_type": "markdown",
2420 |       "source": [
2421 |         "Data Normalization"
2422 |       ],
2423 |       "metadata": {
2424 |         "id": "bpB03y4vSDsB"
2425 |       }
2426 |     },
2427 |     {
2428 |       "cell_type": "code",
2429 |       "source": [
2430 |         "#Import pandas and sklearn library for preprocessing\n",
2431 |         "from sklearn import preprocessing\n",
2432 |         "min_max_scaler = preprocessing.MinMaxScaler()"
2433 |       ],
2434 |       "metadata": {
2435 |         "id": "zIYCseXSSBM6"
2436 |       },
2437 |       "execution_count": 55,
2438 |       "outputs": []
2439 |     },
2440 |     {
2441 |       "cell_type": "code",
2442 |       "source": [
2443 |         "\n",
2444 |         "X=iris.iloc[:,:4]"
2445 |       ],
2446 |       "metadata": {
2447 |         "id": "RS5YUmoBSGaw"
2448 |       },
2449 |       "execution_count": 56,
2450 |       "outputs": []
2451 |     },
2452 |     {
2453 |       "cell_type": "code",
2454 |       "source": [
2455 |         "X"
2456 |       ],
2457 |       "metadata": {
2458 |         "colab": {
2459 |           "base_uri": "https://localhost:8080/",
2460 |           "height": 424
2461 |         },
2462 |         "id": "NQiNEnDzSM38",
2463 |         "outputId": "9675f22e-3930-40b6-dd1c-120329cda471"
2464 |       },
2465 |       "execution_count": 57,
2466 |       "outputs": [
2467 |         {
2468 |           "output_type": "execute_result",
2469 |           "data": {
2470 |             "text/plain": [
2471 |               "     Sepal_Length  Sepal_Width  Petal_Length  Petal_Width\n",
2472 |               "0             5.1          3.5           1.4          0.2\n",
2473 |               "1             4.9          3.0           1.4          0.2\n",
2474 |               "2             4.7          3.2           1.3          0.2\n",
2475 |               "3             4.6          3.1           1.5          0.2\n",
2476 |               "4             5.0          3.6           1.4          0.2\n",
2477 |               "..            ...          ...           ...          ...\n",
2478 |               "145           6.7          3.0           5.2          2.3\n",
2479 |               "146           6.3          2.5           5.0          1.9\n",
2480 |               "147           6.5          3.0           5.2          2.0\n",
2481 |               "148           6.2          3.4           5.4          2.3\n",
2482 |               "149           5.9          3.0           5.1          1.8\n",
2483 |               "\n",
2484 |               "[150 rows x 4 columns]"
2485 |             ],
2486 |             "text/html": [
2487 |               "\n",
2488 |               "  <div id=\"df-eb42f159-3f04-4b47-ad14-88bf30077653\">\n",
2489 |               "    <div class=\"colab-df-container\">\n",
2490 |               "      <div>\n",
2491 |               "<style scoped>\n",
2492 |               "    .dataframe tbody tr th:only-of-type {\n",
2493 |               "        vertical-align: middle;\n",
2494 |               "    }\n",
2495 |               "\n",
2496 |               "    .dataframe tbody tr th {\n",
2497 |               "        vertical-align: top;\n",
2498 |               "    }\n",
2499 |               "\n",
2500 |               "    .dataframe thead th {\n",
2501 |               "        text-align: right;\n",
2502 |               "    }\n",
2503 |               "</style>\n",
2504 |               "<table border=\"1\" class=\"dataframe\">\n",
2505 |               "  <thead>\n",
2506 |               "    <tr style=\"text-align: right;\">\n",
2507 |               "      <th></th>\n",
2508 |               "      <th>Sepal_Length</th>\n",
2509 |               "      <th>Sepal_Width</th>\n",
2510 |               "      <th>Petal_Length</th>\n",
2511 |               "      <th>Petal_Width</th>\n",
2512 |               "    </tr>\n",
2513 |               "  </thead>\n",
2514 |               "  <tbody>\n",
2515 |               "    <tr>\n",
2516 |               "      <th>0</th>\n",
2517 |               "      <td>5.1</td>\n",
2518 |               "      <td>3.5</td>\n",
2519 |               "      <td>1.4</td>\n",
2520 |               "      <td>0.2</td>\n",
2521 |               "    </tr>\n",
2522 |               "    <tr>\n",
2523 |               "      <th>1</th>\n",
2524 |               "      <td>4.9</td>\n",
2525 |               "      <td>3.0</td>\n",
2526 |               "      <td>1.4</td>\n",
2527 |               "      <td>0.2</td>\n",
2528 |               "    </tr>\n",
2529 |               "    <tr>\n",
2530 |               "      <th>2</th>\n",
2531 |               "      <td>4.7</td>\n",
2532 |               "      <td>3.2</td>\n",
2533 |               "      <td>1.3</td>\n",
2534 |               "      <td>0.2</td>\n",
2535 |               "    </tr>\n",
2536 |               "    <tr>\n",
2537 |               "      <th>3</th>\n",
2538 |               "      <td>4.6</td>\n",
2539 |               "      <td>3.1</td>\n",
2540 |               "      <td>1.5</td>\n",
2541 |               "      <td>0.2</td>\n",
2542 |               "    </tr>\n",
2543 |               "    <tr>\n",
2544 |               "      <th>4</th>\n",
2545 |               "      <td>5.0</td>\n",
2546 |               "      <td>3.6</td>\n",
2547 |               "      <td>1.4</td>\n",
2548 |               "      <td>0.2</td>\n",
2549 |               "    </tr>\n",
2550 |               "    <tr>\n",
2551 |               "      <th>...</th>\n",
2552 |               "      <td>...</td>\n",
2553 |               "      <td>...</td>\n",
2554 |               "      <td>...</td>\n",
2555 |               "      <td>...</td>\n",
2556 |               "    </tr>\n",
2557 |               "    <tr>\n",
2558 |               "      <th>145</th>\n",
2559 |               "      <td>6.7</td>\n",
2560 |               "      <td>3.0</td>\n",
2561 |               "      <td>5.2</td>\n",
2562 |               "      <td>2.3</td>\n",
2563 |               "    </tr>\n",
2564 |               "    <tr>\n",
2565 |               "      <th>146</th>\n",
2566 |               "      <td>6.3</td>\n",
2567 |               "      <td>2.5</td>\n",
2568 |               "      <td>5.0</td>\n",
2569 |               "      <td>1.9</td>\n",
2570 |               "    </tr>\n",
2571 |               "    <tr>\n",
2572 |               "      <th>147</th>\n",
2573 |               "      <td>6.5</td>\n",
2574 |               "      <td>3.0</td>\n",
2575 |               "      <td>5.2</td>\n",
2576 |               "      <td>2.0</td>\n",
2577 |               "    </tr>\n",
2578 |               "    <tr>\n",
2579 |               "      <th>148</th>\n",
2580 |               "      <td>6.2</td>\n",
2581 |               "      <td>3.4</td>\n",
2582 |               "      <td>5.4</td>\n",
2583 |               "      <td>2.3</td>\n",
2584 |               "    </tr>\n",
2585 |               "    <tr>\n",
2586 |               "      <th>149</th>\n",
2587 |               "      <td>5.9</td>\n",
2588 |               "      <td>3.0</td>\n",
2589 |               "      <td>5.1</td>\n",
2590 |               "      <td>1.8</td>\n",
2591 |               "    </tr>\n",
2592 |               "  </tbody>\n",
2593 |               "</table>\n",
2594 |               "<p>150 rows × 4 columns</p>\n",
2595 |               "</div>\n",
2596 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb42f159-3f04-4b47-ad14-88bf30077653')\"\n",
2597 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
2598 |               "              style=\"display:none;\">\n",
2599 |               "        \n",
2600 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
2601 |               "       width=\"24px\">\n",
2602 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
2603 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
2604 |               "  </svg>\n",
2605 |               "      </button>\n",
2606 |               "      \n",
2607 |               "  <style>\n",
2608 |               "    .colab-df-container {\n",
2609 |               "      display:flex;\n",
2610 |               "      flex-wrap:wrap;\n",
2611 |               "      gap: 12px;\n",
2612 |               "    }\n",
2613 |               "\n",
2614 |               "    .colab-df-convert {\n",
2615 |               "      background-color: #E8F0FE;\n",
2616 |               "      border: none;\n",
2617 |               "      border-radius: 50%;\n",
2618 |               "      cursor: pointer;\n",
2619 |               "      display: none;\n",
2620 |               "      fill: #1967D2;\n",
2621 |               "      height: 32px;\n",
2622 |               "      padding: 0 0 0 0;\n",
2623 |               "      width: 32px;\n",
2624 |               "    }\n",
2625 |               "\n",
2626 |               "    .colab-df-convert:hover {\n",
2627 |               "      background-color: #E2EBFA;\n",
2628 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
2629 |               "      fill: #174EA6;\n",
2630 |               "    }\n",
2631 |               "\n",
2632 |               "    [theme=dark] .colab-df-convert {\n",
2633 |               "      background-color: #3B4455;\n",
2634 |               "      fill: #D2E3FC;\n",
2635 |               "    }\n",
2636 |               "\n",
2637 |               "    [theme=dark] .colab-df-convert:hover {\n",
2638 |               "      background-color: #434B5C;\n",
2639 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
2640 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
2641 |               "      fill: #FFFFFF;\n",
2642 |               "    }\n",
2643 |               "  </style>\n",
2644 |               "\n",
2645 |               "      <script>\n",
2646 |               "        const buttonEl =\n",
2647 |               "          document.querySelector('#df-eb42f159-3f04-4b47-ad14-88bf30077653 button.colab-df-convert');\n",
2648 |               "        buttonEl.style.display =\n",
2649 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
2650 |               "\n",
2651 |               "        async function convertToInteractive(key) {\n",
2652 |               "          const element = document.querySelector('#df-eb42f159-3f04-4b47-ad14-88bf30077653');\n",
2653 |               "          const dataTable =\n",
2654 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
2655 |               "                                                     [key], {});\n",
2656 |               "          if (!dataTable) return;\n",
2657 |               "\n",
2658 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
2659 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
2660 |               "            + ' to learn more about interactive tables.';\n",
2661 |               "          element.innerHTML = '';\n",
2662 |               "          dataTable['output_type'] = 'display_data';\n",
2663 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
2664 |               "          const docLink = document.createElement('div');\n",
2665 |               "          docLink.innerHTML = docLinkHtml;\n",
2666 |               "          element.appendChild(docLink);\n",
2667 |               "        }\n",
2668 |               "      </script>\n",
2669 |               "    </div>\n",
2670 |               "  </div>\n",
2671 |               "  "
2672 |             ]
2673 |           },
2674 |           "metadata": {},
2675 |           "execution_count": 57
2676 |         }
2677 |       ]
2678 |     },
2679 |     {
2680 |       "cell_type": "code",
2681 |       "source": [
2682 |         "# Create an object to transfrom the data to fit minmax processor\n",
2683 |         "X_scaled = min_max_scaler.fit_transform(X)"
2684 |       ],
2685 |       "metadata": {
2686 |         "id": "evPPvj5rSPkl"
2687 |       },
2688 |       "execution_count": 58,
2689 |       "outputs": []
2690 |     },
2691 |     {
2692 |       "cell_type": "code",
2693 |       "source": [
2694 |         "df_normalized = pd.DataFrame(X_scaled)"
2695 |       ],
2696 |       "metadata": {
2697 |         "id": "2l__PfG6SSqn"
2698 |       },
2699 |       "execution_count": 59,
2700 |       "outputs": []
2701 |     },
2702 |     {
2703 |       "cell_type": "code",
2704 |       "source": [
2705 |         "df_normalized"
2706 |       ],
2707 |       "metadata": {
2708 |         "colab": {
2709 |           "base_uri": "https://localhost:8080/",
2710 |           "height": 424
2711 |         },
2712 |         "id": "NST1NBIESVXB",
2713 |         "outputId": "e3db8b46-905b-4029-e058-16a92b023dfc"
2714 |       },
2715 |       "execution_count": 60,
2716 |       "outputs": [
2717 |         {
2718 |           "output_type": "execute_result",
2719 |           "data": {
2720 |             "text/plain": [
2721 |               "            0         1         2         3\n",
2722 |               "0    0.222222  0.625000  0.067797  0.041667\n",
2723 |               "1    0.166667  0.416667  0.067797  0.041667\n",
2724 |               "2    0.111111  0.500000  0.050847  0.041667\n",
2725 |               "3    0.083333  0.458333  0.084746  0.041667\n",
2726 |               "4    0.194444  0.666667  0.067797  0.041667\n",
2727 |               "..        ...       ...       ...       ...\n",
2728 |               "145  0.666667  0.416667  0.711864  0.916667\n",
2729 |               "146  0.555556  0.208333  0.677966  0.750000\n",
2730 |               "147  0.611111  0.416667  0.711864  0.791667\n",
2731 |               "148  0.527778  0.583333  0.745763  0.916667\n",
2732 |               "149  0.444444  0.416667  0.694915  0.708333\n",
2733 |               "\n",
2734 |               "[150 rows x 4 columns]"
2735 |             ],
2736 |             "text/html": [
2737 |               "\n",
2738 |               "  <div id=\"df-2a563386-00fd-4c34-aca7-9611b18f6622\">\n",
2739 |               "    <div class=\"colab-df-container\">\n",
2740 |               "      <div>\n",
2741 |               "<style scoped>\n",
2742 |               "    .dataframe tbody tr th:only-of-type {\n",
2743 |               "        vertical-align: middle;\n",
2744 |               "    }\n",
2745 |               "\n",
2746 |               "    .dataframe tbody tr th {\n",
2747 |               "        vertical-align: top;\n",
2748 |               "    }\n",
2749 |               "\n",
2750 |               "    .dataframe thead th {\n",
2751 |               "        text-align: right;\n",
2752 |               "    }\n",
2753 |               "</style>\n",
2754 |               "<table border=\"1\" class=\"dataframe\">\n",
2755 |               "  <thead>\n",
2756 |               "    <tr style=\"text-align: right;\">\n",
2757 |               "      <th></th>\n",
2758 |               "      <th>0</th>\n",
2759 |               "      <th>1</th>\n",
2760 |               "      <th>2</th>\n",
2761 |               "      <th>3</th>\n",
2762 |               "    </tr>\n",
2763 |               "  </thead>\n",
2764 |               "  <tbody>\n",
2765 |               "    <tr>\n",
2766 |               "      <th>0</th>\n",
2767 |               "      <td>0.222222</td>\n",
2768 |               "      <td>0.625000</td>\n",
2769 |               "      <td>0.067797</td>\n",
2770 |               "      <td>0.041667</td>\n",
2771 |               "    </tr>\n",
2772 |               "    <tr>\n",
2773 |               "      <th>1</th>\n",
2774 |               "      <td>0.166667</td>\n",
2775 |               "      <td>0.416667</td>\n",
2776 |               "      <td>0.067797</td>\n",
2777 |               "      <td>0.041667</td>\n",
2778 |               "    </tr>\n",
2779 |               "    <tr>\n",
2780 |               "      <th>2</th>\n",
2781 |               "      <td>0.111111</td>\n",
2782 |               "      <td>0.500000</td>\n",
2783 |               "      <td>0.050847</td>\n",
2784 |               "      <td>0.041667</td>\n",
2785 |               "    </tr>\n",
2786 |               "    <tr>\n",
2787 |               "      <th>3</th>\n",
2788 |               "      <td>0.083333</td>\n",
2789 |               "      <td>0.458333</td>\n",
2790 |               "      <td>0.084746</td>\n",
2791 |               "      <td>0.041667</td>\n",
2792 |               "    </tr>\n",
2793 |               "    <tr>\n",
2794 |               "      <th>4</th>\n",
2795 |               "      <td>0.194444</td>\n",
2796 |               "      <td>0.666667</td>\n",
2797 |               "      <td>0.067797</td>\n",
2798 |               "      <td>0.041667</td>\n",
2799 |               "    </tr>\n",
2800 |               "    <tr>\n",
2801 |               "      <th>...</th>\n",
2802 |               "      <td>...</td>\n",
2803 |               "      <td>...</td>\n",
2804 |               "      <td>...</td>\n",
2805 |               "      <td>...</td>\n",
2806 |               "    </tr>\n",
2807 |               "    <tr>\n",
2808 |               "      <th>145</th>\n",
2809 |               "      <td>0.666667</td>\n",
2810 |               "      <td>0.416667</td>\n",
2811 |               "      <td>0.711864</td>\n",
2812 |               "      <td>0.916667</td>\n",
2813 |               "    </tr>\n",
2814 |               "    <tr>\n",
2815 |               "      <th>146</th>\n",
2816 |               "      <td>0.555556</td>\n",
2817 |               "      <td>0.208333</td>\n",
2818 |               "      <td>0.677966</td>\n",
2819 |               "      <td>0.750000</td>\n",
2820 |               "    </tr>\n",
2821 |               "    <tr>\n",
2822 |               "      <th>147</th>\n",
2823 |               "      <td>0.611111</td>\n",
2824 |               "      <td>0.416667</td>\n",
2825 |               "      <td>0.711864</td>\n",
2826 |               "      <td>0.791667</td>\n",
2827 |               "    </tr>\n",
2828 |               "    <tr>\n",
2829 |               "      <th>148</th>\n",
2830 |               "      <td>0.527778</td>\n",
2831 |               "      <td>0.583333</td>\n",
2832 |               "      <td>0.745763</td>\n",
2833 |               "      <td>0.916667</td>\n",
2834 |               "    </tr>\n",
2835 |               "    <tr>\n",
2836 |               "      <th>149</th>\n",
2837 |               "      <td>0.444444</td>\n",
2838 |               "      <td>0.416667</td>\n",
2839 |               "      <td>0.694915</td>\n",
2840 |               "      <td>0.708333</td>\n",
2841 |               "    </tr>\n",
2842 |               "  </tbody>\n",
2843 |               "</table>\n",
2844 |               "<p>150 rows × 4 columns</p>\n",
2845 |               "</div>\n",
2846 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a563386-00fd-4c34-aca7-9611b18f6622')\"\n",
2847 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
2848 |               "              style=\"display:none;\">\n",
2849 |               "        \n",
2850 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
2851 |               "       width=\"24px\">\n",
2852 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
2853 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
2854 |               "  </svg>\n",
2855 |               "      </button>\n",
2856 |               "      \n",
2857 |               "  <style>\n",
2858 |               "    .colab-df-container {\n",
2859 |               "      display:flex;\n",
2860 |               "      flex-wrap:wrap;\n",
2861 |               "      gap: 12px;\n",
2862 |               "    }\n",
2863 |               "\n",
2864 |               "    .colab-df-convert {\n",
2865 |               "      background-color: #E8F0FE;\n",
2866 |               "      border: none;\n",
2867 |               "      border-radius: 50%;\n",
2868 |               "      cursor: pointer;\n",
2869 |               "      display: none;\n",
2870 |               "      fill: #1967D2;\n",
2871 |               "      height: 32px;\n",
2872 |               "      padding: 0 0 0 0;\n",
2873 |               "      width: 32px;\n",
2874 |               "    }\n",
2875 |               "\n",
2876 |               "    .colab-df-convert:hover {\n",
2877 |               "      background-color: #E2EBFA;\n",
2878 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
2879 |               "      fill: #174EA6;\n",
2880 |               "    }\n",
2881 |               "\n",
2882 |               "    [theme=dark] .colab-df-convert {\n",
2883 |               "      background-color: #3B4455;\n",
2884 |               "      fill: #D2E3FC;\n",
2885 |               "    }\n",
2886 |               "\n",
2887 |               "    [theme=dark] .colab-df-convert:hover {\n",
2888 |               "      background-color: #434B5C;\n",
2889 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
2890 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
2891 |               "      fill: #FFFFFF;\n",
2892 |               "    }\n",
2893 |               "  </style>\n",
2894 |               "\n",
2895 |               "      <script>\n",
2896 |               "        const buttonEl =\n",
2897 |               "          document.querySelector('#df-2a563386-00fd-4c34-aca7-9611b18f6622 button.colab-df-convert');\n",
2898 |               "        buttonEl.style.display =\n",
2899 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
2900 |               "\n",
2901 |               "        async function convertToInteractive(key) {\n",
2902 |               "          const element = document.querySelector('#df-2a563386-00fd-4c34-aca7-9611b18f6622');\n",
2903 |               "          const dataTable =\n",
2904 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
2905 |               "                                                     [key], {});\n",
2906 |               "          if (!dataTable) return;\n",
2907 |               "\n",
2908 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
2909 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
2910 |               "            + ' to learn more about interactive tables.';\n",
2911 |               "          element.innerHTML = '';\n",
2912 |               "          dataTable['output_type'] = 'display_data';\n",
2913 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
2914 |               "          const docLink = document.createElement('div');\n",
2915 |               "          docLink.innerHTML = docLinkHtml;\n",
2916 |               "          element.appendChild(docLink);\n",
2917 |               "        }\n",
2918 |               "      </script>\n",
2919 |               "    </div>\n",
2920 |               "  </div>\n",
2921 |               "  "
2922 |             ]
2923 |           },
2924 |           "metadata": {},
2925 |           "execution_count": 60
2926 |         }
2927 |       ]
2928 |     },
2929 |     {
2930 |       "cell_type": "markdown",
2931 |       "source": [
2932 |         "Handaling Categorical Variables"
2933 |       ],
2934 |       "metadata": {
2935 |         "id": "dsuQ5tFFSaXn"
2936 |       }
2937 |     },
2938 |     {
2939 |       "cell_type": "code",
2940 |       "source": [
2941 |         "df2=df\n",
2942 |         "df2['Species'].unique()"
2943 |       ],
2944 |       "metadata": {
2945 |         "colab": {
2946 |           "base_uri": "https://localhost:8080/"
2947 |         },
2948 |         "id": "kYQM_iJoSYEV",
2949 |         "outputId": "c80f8cda-f230-4035-dae0-618550c8c9c7"
2950 |       },
2951 |       "execution_count": 61,
2952 |       "outputs": [
2953 |         {
2954 |           "output_type": "execute_result",
2955 |           "data": {
2956 |             "text/plain": [
2957 |               "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
2958 |             ]
2959 |           },
2960 |           "metadata": {},
2961 |           "execution_count": 61
2962 |         }
2963 |       ]
2964 |     },
2965 |     {
2966 |       "cell_type": "code",
2967 |       "source": [
2968 |         "from sklearn import preprocessing\n",
2969 |         "enc = preprocessing.OneHotEncoder()"
2970 |       ],
2971 |       "metadata": {
2972 |         "id": "hxRkilNdSfyR"
2973 |       },
2974 |       "execution_count": 62,
2975 |       "outputs": []
2976 |     },
2977 |     {
2978 |       "cell_type": "code",
2979 |       "source": [
2980 |         "features_df=df2.drop(columns=['Species'])"
2981 |       ],
2982 |       "metadata": {
2983 |         "id": "WI-k4YvRSnOR"
2984 |       },
2985 |       "execution_count": 63,
2986 |       "outputs": []
2987 |     },
2988 |     {
2989 |       "cell_type": "code",
2990 |       "source": [
2991 |         "features_df"
2992 |       ],
2993 |       "metadata": {
2994 |         "colab": {
2995 |           "base_uri": "https://localhost:8080/",
2996 |           "height": 424
2997 |         },
2998 |         "id": "eW4mOi_VSqK0",
2999 |         "outputId": "741f8b00-3030-4cdc-9652-dfacba54fe37"
3000 |       },
3001 |       "execution_count": 64,
3002 |       "outputs": [
3003 |         {
3004 |           "output_type": "execute_result",
3005 |           "data": {
3006 |             "text/plain": [
3007 |               "     Sepal_Length  Sepal_Width  Petal_Length  Petal_Width  petal Length(cm)\n",
3008 |               "0             5.1          3.5           1.4          0.2                 1\n",
3009 |               "1             4.9          3.0           1.4          0.2                 1\n",
3010 |               "2             4.7          3.2           1.3          0.2                 1\n",
3011 |               "3             4.6          3.1           1.5          0.2                 1\n",
3012 |               "4             5.0          3.6           1.4          0.2                 1\n",
3013 |               "..            ...          ...           ...          ...               ...\n",
3014 |               "145           6.7          3.0           5.2          2.3                 5\n",
3015 |               "146           6.3          2.5           5.0          1.9                 5\n",
3016 |               "147           6.5          3.0           5.2          2.0                 5\n",
3017 |               "148           6.2          3.4           5.4          2.3                 5\n",
3018 |               "149           5.9          3.0           5.1          1.8                 5\n",
3019 |               "\n",
3020 |               "[150 rows x 5 columns]"
3021 |             ],
3022 |             "text/html": [
3023 |               "\n",
3024 |               "  <div id=\"df-077fed86-7290-4949-86a5-79d3eabf0450\">\n",
3025 |               "    <div class=\"colab-df-container\">\n",
3026 |               "      <div>\n",
3027 |               "<style scoped>\n",
3028 |               "    .dataframe tbody tr th:only-of-type {\n",
3029 |               "        vertical-align: middle;\n",
3030 |               "    }\n",
3031 |               "\n",
3032 |               "    .dataframe tbody tr th {\n",
3033 |               "        vertical-align: top;\n",
3034 |               "    }\n",
3035 |               "\n",
3036 |               "    .dataframe thead th {\n",
3037 |               "        text-align: right;\n",
3038 |               "    }\n",
3039 |               "</style>\n",
3040 |               "<table border=\"1\" class=\"dataframe\">\n",
3041 |               "  <thead>\n",
3042 |               "    <tr style=\"text-align: right;\">\n",
3043 |               "      <th></th>\n",
3044 |               "      <th>Sepal_Length</th>\n",
3045 |               "      <th>Sepal_Width</th>\n",
3046 |               "      <th>Petal_Length</th>\n",
3047 |               "      <th>Petal_Width</th>\n",
3048 |               "      <th>petal Length(cm)</th>\n",
3049 |               "    </tr>\n",
3050 |               "  </thead>\n",
3051 |               "  <tbody>\n",
3052 |               "    <tr>\n",
3053 |               "      <th>0</th>\n",
3054 |               "      <td>5.1</td>\n",
3055 |               "      <td>3.5</td>\n",
3056 |               "      <td>1.4</td>\n",
3057 |               "      <td>0.2</td>\n",
3058 |               "      <td>1</td>\n",
3059 |               "    </tr>\n",
3060 |               "    <tr>\n",
3061 |               "      <th>1</th>\n",
3062 |               "      <td>4.9</td>\n",
3063 |               "      <td>3.0</td>\n",
3064 |               "      <td>1.4</td>\n",
3065 |               "      <td>0.2</td>\n",
3066 |               "      <td>1</td>\n",
3067 |               "    </tr>\n",
3068 |               "    <tr>\n",
3069 |               "      <th>2</th>\n",
3070 |               "      <td>4.7</td>\n",
3071 |               "      <td>3.2</td>\n",
3072 |               "      <td>1.3</td>\n",
3073 |               "      <td>0.2</td>\n",
3074 |               "      <td>1</td>\n",
3075 |               "    </tr>\n",
3076 |               "    <tr>\n",
3077 |               "      <th>3</th>\n",
3078 |               "      <td>4.6</td>\n",
3079 |               "      <td>3.1</td>\n",
3080 |               "      <td>1.5</td>\n",
3081 |               "      <td>0.2</td>\n",
3082 |               "      <td>1</td>\n",
3083 |               "    </tr>\n",
3084 |               "    <tr>\n",
3085 |               "      <th>4</th>\n",
3086 |               "      <td>5.0</td>\n",
3087 |               "      <td>3.6</td>\n",
3088 |               "      <td>1.4</td>\n",
3089 |               "      <td>0.2</td>\n",
3090 |               "      <td>1</td>\n",
3091 |               "    </tr>\n",
3092 |               "    <tr>\n",
3093 |               "      <th>...</th>\n",
3094 |               "      <td>...</td>\n",
3095 |               "      <td>...</td>\n",
3096 |               "      <td>...</td>\n",
3097 |               "      <td>...</td>\n",
3098 |               "      <td>...</td>\n",
3099 |               "    </tr>\n",
3100 |               "    <tr>\n",
3101 |               "      <th>145</th>\n",
3102 |               "      <td>6.7</td>\n",
3103 |               "      <td>3.0</td>\n",
3104 |               "      <td>5.2</td>\n",
3105 |               "      <td>2.3</td>\n",
3106 |               "      <td>5</td>\n",
3107 |               "    </tr>\n",
3108 |               "    <tr>\n",
3109 |               "      <th>146</th>\n",
3110 |               "      <td>6.3</td>\n",
3111 |               "      <td>2.5</td>\n",
3112 |               "      <td>5.0</td>\n",
3113 |               "      <td>1.9</td>\n",
3114 |               "      <td>5</td>\n",
3115 |               "    </tr>\n",
3116 |               "    <tr>\n",
3117 |               "      <th>147</th>\n",
3118 |               "      <td>6.5</td>\n",
3119 |               "      <td>3.0</td>\n",
3120 |               "      <td>5.2</td>\n",
3121 |               "      <td>2.0</td>\n",
3122 |               "      <td>5</td>\n",
3123 |               "    </tr>\n",
3124 |               "    <tr>\n",
3125 |               "      <th>148</th>\n",
3126 |               "      <td>6.2</td>\n",
3127 |               "      <td>3.4</td>\n",
3128 |               "      <td>5.4</td>\n",
3129 |               "      <td>2.3</td>\n",
3130 |               "      <td>5</td>\n",
3131 |               "    </tr>\n",
3132 |               "    <tr>\n",
3133 |               "      <th>149</th>\n",
3134 |               "      <td>5.9</td>\n",
3135 |               "      <td>3.0</td>\n",
3136 |               "      <td>5.1</td>\n",
3137 |               "      <td>1.8</td>\n",
3138 |               "      <td>5</td>\n",
3139 |               "    </tr>\n",
3140 |               "  </tbody>\n",
3141 |               "</table>\n",
3142 |               "<p>150 rows × 5 columns</p>\n",
3143 |               "</div>\n",
3144 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-077fed86-7290-4949-86a5-79d3eabf0450')\"\n",
3145 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
3146 |               "              style=\"display:none;\">\n",
3147 |               "        \n",
3148 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
3149 |               "       width=\"24px\">\n",
3150 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
3151 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
3152 |               "  </svg>\n",
3153 |               "      </button>\n",
3154 |               "      \n",
3155 |               "  <style>\n",
3156 |               "    .colab-df-container {\n",
3157 |               "      display:flex;\n",
3158 |               "      flex-wrap:wrap;\n",
3159 |               "      gap: 12px;\n",
3160 |               "    }\n",
3161 |               "\n",
3162 |               "    .colab-df-convert {\n",
3163 |               "      background-color: #E8F0FE;\n",
3164 |               "      border: none;\n",
3165 |               "      border-radius: 50%;\n",
3166 |               "      cursor: pointer;\n",
3167 |               "      display: none;\n",
3168 |               "      fill: #1967D2;\n",
3169 |               "      height: 32px;\n",
3170 |               "      padding: 0 0 0 0;\n",
3171 |               "      width: 32px;\n",
3172 |               "    }\n",
3173 |               "\n",
3174 |               "    .colab-df-convert:hover {\n",
3175 |               "      background-color: #E2EBFA;\n",
3176 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
3177 |               "      fill: #174EA6;\n",
3178 |               "    }\n",
3179 |               "\n",
3180 |               "    [theme=dark] .colab-df-convert {\n",
3181 |               "      background-color: #3B4455;\n",
3182 |               "      fill: #D2E3FC;\n",
3183 |               "    }\n",
3184 |               "\n",
3185 |               "    [theme=dark] .colab-df-convert:hover {\n",
3186 |               "      background-color: #434B5C;\n",
3187 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
3188 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
3189 |               "      fill: #FFFFFF;\n",
3190 |               "    }\n",
3191 |               "  </style>\n",
3192 |               "\n",
3193 |               "      <script>\n",
3194 |               "        const buttonEl =\n",
3195 |               "          document.querySelector('#df-077fed86-7290-4949-86a5-79d3eabf0450 button.colab-df-convert');\n",
3196 |               "        buttonEl.style.display =\n",
3197 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
3198 |               "\n",
3199 |               "        async function convertToInteractive(key) {\n",
3200 |               "          const element = document.querySelector('#df-077fed86-7290-4949-86a5-79d3eabf0450');\n",
3201 |               "          const dataTable =\n",
3202 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
3203 |               "                                                     [key], {});\n",
3204 |               "          if (!dataTable) return;\n",
3205 |               "\n",
3206 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
3207 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
3208 |               "            + ' to learn more about interactive tables.';\n",
3209 |               "          element.innerHTML = '';\n",
3210 |               "          dataTable['output_type'] = 'display_data';\n",
3211 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
3212 |               "          const docLink = document.createElement('div');\n",
3213 |               "          docLink.innerHTML = docLinkHtml;\n",
3214 |               "          element.appendChild(docLink);\n",
3215 |               "        }\n",
3216 |               "      </script>\n",
3217 |               "    </div>\n",
3218 |               "  </div>\n",
3219 |               "  "
3220 |             ]
3221 |           },
3222 |           "metadata": {},
3223 |           "execution_count": 64
3224 |         }
3225 |       ]
3226 |     },
3227 |     {
3228 |       "cell_type": "code",
3229 |       "source": [
3230 |         "enc_df=(enc.fit_transform(df2[['Species']])).toarray()\n",
3231 |         "     \n"
3232 |       ],
3233 |       "metadata": {
3234 |         "id": "FLjh34boStCG"
3235 |       },
3236 |       "execution_count": 65,
3237 |       "outputs": []
3238 |     },
3239 |     {
3240 |       "cell_type": "code",
3241 |       "source": [
3242 |         "\n",
3243 |         "enc_df = pd.DataFrame(enc_df, columns = ['Iris-Setosa','Iris-Versicolor','Iris-Virginica'])"
3244 |       ],
3245 |       "metadata": {
3246 |         "id": "CVaiaYtOSwqs"
3247 |       },
3248 |       "execution_count": 66,
3249 |       "outputs": []
3250 |     },
3251 |     {
3252 |       "cell_type": "code",
3253 |       "source": [
3254 |         "df_encode = features_df.join(enc_df)"
3255 |       ],
3256 |       "metadata": {
3257 |         "id": "GlZ4eFowSz4f"
3258 |       },
3259 |       "execution_count": 67,
3260 |       "outputs": []
3261 |     },
3262 |     {
3263 |       "cell_type": "code",
3264 |       "source": [
3265 |         "df_encode"
3266 |       ],
3267 |       "metadata": {
3268 |         "colab": {
3269 |           "base_uri": "https://localhost:8080/",
3270 |           "height": 424
3271 |         },
3272 |         "id": "vzebBKUFS2U7",
3273 |         "outputId": "dcd6ddab-d283-4004-d72e-cdf603007b74"
3274 |       },
3275 |       "execution_count": 68,
3276 |       "outputs": [
3277 |         {
3278 |           "output_type": "execute_result",
3279 |           "data": {
3280 |             "text/plain": [
3281 |               "     Sepal_Length  Sepal_Width  Petal_Length  Petal_Width  petal Length(cm)  \\\n",
3282 |               "0             5.1          3.5           1.4          0.2                 1   \n",
3283 |               "1             4.9          3.0           1.4          0.2                 1   \n",
3284 |               "2             4.7          3.2           1.3          0.2                 1   \n",
3285 |               "3             4.6          3.1           1.5          0.2                 1   \n",
3286 |               "4             5.0          3.6           1.4          0.2                 1   \n",
3287 |               "..            ...          ...           ...          ...               ...   \n",
3288 |               "145           6.7          3.0           5.2          2.3                 5   \n",
3289 |               "146           6.3          2.5           5.0          1.9                 5   \n",
3290 |               "147           6.5          3.0           5.2          2.0                 5   \n",
3291 |               "148           6.2          3.4           5.4          2.3                 5   \n",
3292 |               "149           5.9          3.0           5.1          1.8                 5   \n",
3293 |               "\n",
3294 |               "     Iris-Setosa  Iris-Versicolor  Iris-Virginica  \n",
3295 |               "0            1.0              0.0             0.0  \n",
3296 |               "1            1.0              0.0             0.0  \n",
3297 |               "2            1.0              0.0             0.0  \n",
3298 |               "3            1.0              0.0             0.0  \n",
3299 |               "4            1.0              0.0             0.0  \n",
3300 |               "..           ...              ...             ...  \n",
3301 |               "145          0.0              0.0             1.0  \n",
3302 |               "146          0.0              0.0             1.0  \n",
3303 |               "147          0.0              0.0             1.0  \n",
3304 |               "148          0.0              0.0             1.0  \n",
3305 |               "149          0.0              0.0             1.0  \n",
3306 |               "\n",
3307 |               "[150 rows x 8 columns]"
3308 |             ],
3309 |             "text/html": [
3310 |               "\n",
3311 |               "  <div id=\"df-a47c4062-3d08-448d-b07e-c381708598ab\">\n",
3312 |               "    <div class=\"colab-df-container\">\n",
3313 |               "      <div>\n",
3314 |               "<style scoped>\n",
3315 |               "    .dataframe tbody tr th:only-of-type {\n",
3316 |               "        vertical-align: middle;\n",
3317 |               "    }\n",
3318 |               "\n",
3319 |               "    .dataframe tbody tr th {\n",
3320 |               "        vertical-align: top;\n",
3321 |               "    }\n",
3322 |               "\n",
3323 |               "    .dataframe thead th {\n",
3324 |               "        text-align: right;\n",
3325 |               "    }\n",
3326 |               "</style>\n",
3327 |               "<table border=\"1\" class=\"dataframe\">\n",
3328 |               "  <thead>\n",
3329 |               "    <tr style=\"text-align: right;\">\n",
3330 |               "      <th></th>\n",
3331 |               "      <th>Sepal_Length</th>\n",
3332 |               "      <th>Sepal_Width</th>\n",
3333 |               "      <th>Petal_Length</th>\n",
3334 |               "      <th>Petal_Width</th>\n",
3335 |               "      <th>petal Length(cm)</th>\n",
3336 |               "      <th>Iris-Setosa</th>\n",
3337 |               "      <th>Iris-Versicolor</th>\n",
3338 |               "      <th>Iris-Virginica</th>\n",
3339 |               "    </tr>\n",
3340 |               "  </thead>\n",
3341 |               "  <tbody>\n",
3342 |               "    <tr>\n",
3343 |               "      <th>0</th>\n",
3344 |               "      <td>5.1</td>\n",
3345 |               "      <td>3.5</td>\n",
3346 |               "      <td>1.4</td>\n",
3347 |               "      <td>0.2</td>\n",
3348 |               "      <td>1</td>\n",
3349 |               "      <td>1.0</td>\n",
3350 |               "      <td>0.0</td>\n",
3351 |               "      <td>0.0</td>\n",
3352 |               "    </tr>\n",
3353 |               "    <tr>\n",
3354 |               "      <th>1</th>\n",
3355 |               "      <td>4.9</td>\n",
3356 |               "      <td>3.0</td>\n",
3357 |               "      <td>1.4</td>\n",
3358 |               "      <td>0.2</td>\n",
3359 |               "      <td>1</td>\n",
3360 |               "      <td>1.0</td>\n",
3361 |               "      <td>0.0</td>\n",
3362 |               "      <td>0.0</td>\n",
3363 |               "    </tr>\n",
3364 |               "    <tr>\n",
3365 |               "      <th>2</th>\n",
3366 |               "      <td>4.7</td>\n",
3367 |               "      <td>3.2</td>\n",
3368 |               "      <td>1.3</td>\n",
3369 |               "      <td>0.2</td>\n",
3370 |               "      <td>1</td>\n",
3371 |               "      <td>1.0</td>\n",
3372 |               "      <td>0.0</td>\n",
3373 |               "      <td>0.0</td>\n",
3374 |               "    </tr>\n",
3375 |               "    <tr>\n",
3376 |               "      <th>3</th>\n",
3377 |               "      <td>4.6</td>\n",
3378 |               "      <td>3.1</td>\n",
3379 |               "      <td>1.5</td>\n",
3380 |               "      <td>0.2</td>\n",
3381 |               "      <td>1</td>\n",
3382 |               "      <td>1.0</td>\n",
3383 |               "      <td>0.0</td>\n",
3384 |               "      <td>0.0</td>\n",
3385 |               "    </tr>\n",
3386 |               "    <tr>\n",
3387 |               "      <th>4</th>\n",
3388 |               "      <td>5.0</td>\n",
3389 |               "      <td>3.6</td>\n",
3390 |               "      <td>1.4</td>\n",
3391 |               "      <td>0.2</td>\n",
3392 |               "      <td>1</td>\n",
3393 |               "      <td>1.0</td>\n",
3394 |               "      <td>0.0</td>\n",
3395 |               "      <td>0.0</td>\n",
3396 |               "    </tr>\n",
3397 |               "    <tr>\n",
3398 |               "      <th>...</th>\n",
3399 |               "      <td>...</td>\n",
3400 |               "      <td>...</td>\n",
3401 |               "      <td>...</td>\n",
3402 |               "      <td>...</td>\n",
3403 |               "      <td>...</td>\n",
3404 |               "      <td>...</td>\n",
3405 |               "      <td>...</td>\n",
3406 |               "      <td>...</td>\n",
3407 |               "    </tr>\n",
3408 |               "    <tr>\n",
3409 |               "      <th>145</th>\n",
3410 |               "      <td>6.7</td>\n",
3411 |               "      <td>3.0</td>\n",
3412 |               "      <td>5.2</td>\n",
3413 |               "      <td>2.3</td>\n",
3414 |               "      <td>5</td>\n",
3415 |               "      <td>0.0</td>\n",
3416 |               "      <td>0.0</td>\n",
3417 |               "      <td>1.0</td>\n",
3418 |               "    </tr>\n",
3419 |               "    <tr>\n",
3420 |               "      <th>146</th>\n",
3421 |               "      <td>6.3</td>\n",
3422 |               "      <td>2.5</td>\n",
3423 |               "      <td>5.0</td>\n",
3424 |               "      <td>1.9</td>\n",
3425 |               "      <td>5</td>\n",
3426 |               "      <td>0.0</td>\n",
3427 |               "      <td>0.0</td>\n",
3428 |               "      <td>1.0</td>\n",
3429 |               "    </tr>\n",
3430 |               "    <tr>\n",
3431 |               "      <th>147</th>\n",
3432 |               "      <td>6.5</td>\n",
3433 |               "      <td>3.0</td>\n",
3434 |               "      <td>5.2</td>\n",
3435 |               "      <td>2.0</td>\n",
3436 |               "      <td>5</td>\n",
3437 |               "      <td>0.0</td>\n",
3438 |               "      <td>0.0</td>\n",
3439 |               "      <td>1.0</td>\n",
3440 |               "    </tr>\n",
3441 |               "    <tr>\n",
3442 |               "      <th>148</th>\n",
3443 |               "      <td>6.2</td>\n",
3444 |               "      <td>3.4</td>\n",
3445 |               "      <td>5.4</td>\n",
3446 |               "      <td>2.3</td>\n",
3447 |               "      <td>5</td>\n",
3448 |               "      <td>0.0</td>\n",
3449 |               "      <td>0.0</td>\n",
3450 |               "      <td>1.0</td>\n",
3451 |               "    </tr>\n",
3452 |               "    <tr>\n",
3453 |               "      <th>149</th>\n",
3454 |               "      <td>5.9</td>\n",
3455 |               "      <td>3.0</td>\n",
3456 |               "      <td>5.1</td>\n",
3457 |               "      <td>1.8</td>\n",
3458 |               "      <td>5</td>\n",
3459 |               "      <td>0.0</td>\n",
3460 |               "      <td>0.0</td>\n",
3461 |               "      <td>1.0</td>\n",
3462 |               "    </tr>\n",
3463 |               "  </tbody>\n",
3464 |               "</table>\n",
3465 |               "<p>150 rows × 8 columns</p>\n",
3466 |               "</div>\n",
3467 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a47c4062-3d08-448d-b07e-c381708598ab')\"\n",
3468 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
3469 |               "              style=\"display:none;\">\n",
3470 |               "        \n",
3471 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
3472 |               "       width=\"24px\">\n",
3473 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
3474 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
3475 |               "  </svg>\n",
3476 |               "      </button>\n",
3477 |               "      \n",
3478 |               "  <style>\n",
3479 |               "    .colab-df-container {\n",
3480 |               "      display:flex;\n",
3481 |               "      flex-wrap:wrap;\n",
3482 |               "      gap: 12px;\n",
3483 |               "    }\n",
3484 |               "\n",
3485 |               "    .colab-df-convert {\n",
3486 |               "      background-color: #E8F0FE;\n",
3487 |               "      border: none;\n",
3488 |               "      border-radius: 50%;\n",
3489 |               "      cursor: pointer;\n",
3490 |               "      display: none;\n",
3491 |               "      fill: #1967D2;\n",
3492 |               "      height: 32px;\n",
3493 |               "      padding: 0 0 0 0;\n",
3494 |               "      width: 32px;\n",
3495 |               "    }\n",
3496 |               "\n",
3497 |               "    .colab-df-convert:hover {\n",
3498 |               "      background-color: #E2EBFA;\n",
3499 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
3500 |               "      fill: #174EA6;\n",
3501 |               "    }\n",
3502 |               "\n",
3503 |               "    [theme=dark] .colab-df-convert {\n",
3504 |               "      background-color: #3B4455;\n",
3505 |               "      fill: #D2E3FC;\n",
3506 |               "    }\n",
3507 |               "\n",
3508 |               "    [theme=dark] .colab-df-convert:hover {\n",
3509 |               "      background-color: #434B5C;\n",
3510 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
3511 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
3512 |               "      fill: #FFFFFF;\n",
3513 |               "    }\n",
3514 |               "  </style>\n",
3515 |               "\n",
3516 |               "      <script>\n",
3517 |               "        const buttonEl =\n",
3518 |               "          document.querySelector('#df-a47c4062-3d08-448d-b07e-c381708598ab button.colab-df-convert');\n",
3519 |               "        buttonEl.style.display =\n",
3520 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
3521 |               "\n",
3522 |               "        async function convertToInteractive(key) {\n",
3523 |               "          const element = document.querySelector('#df-a47c4062-3d08-448d-b07e-c381708598ab');\n",
3524 |               "          const dataTable =\n",
3525 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
3526 |               "                                                     [key], {});\n",
3527 |               "          if (!dataTable) return;\n",
3528 |               "\n",
3529 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
3530 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
3531 |               "            + ' to learn more about interactive tables.';\n",
3532 |               "          element.innerHTML = '';\n",
3533 |               "          dataTable['output_type'] = 'display_data';\n",
3534 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
3535 |               "          const docLink = document.createElement('div');\n",
3536 |               "          docLink.innerHTML = docLinkHtml;\n",
3537 |               "          element.appendChild(docLink);\n",
3538 |               "        }\n",
3539 |               "      </script>\n",
3540 |               "    </div>\n",
3541 |               "  </div>\n",
3542 |               "  "
3543 |             ]
3544 |           },
3545 |           "metadata": {},
3546 |           "execution_count": 68
3547 |         }
3548 |       ]
3549 |     }
3550 |   ]
3551 | }


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