darkStar3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://towardsdatascience.com/unsupervised-machine-learning-spectral-clustering-algorithm-implemented-from-scratch-in-python-205c87271045"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "#from wordcloud import WordCloud\n",
    "import re\n",
    "import os\n",
    "from sqlalchemy import create_engine # database connection\n",
    "import datetime as dt\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import f1_score,precision_score,recall_score\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from datetime import datetime\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "from sklearn.cluster import SpectralClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "float_formatter = lambda x: \"%.3f\" % x\n",
    "np.set_printoptions(formatter={'float_kind':float_formatter})\n",
    "from sklearn.datasets import make_circles\n",
    "from sklearn.cluster import SpectralClustering, KMeans\n",
    "from sklearn.metrics import pairwise_distances\n",
    "from matplotlib import pyplot as plt\n",
    "import networkx as nx\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from tqdm.notebook import tqdm\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "          2596     51     3    258     0    510    221    232    148    6279  \\\n",
       "0          NaN   56.0   2.0  212.0  -6.0    NaN    NaN  235.0    NaN     NaN   \n",
       "1       2804.0    NaN   NaN  268.0   NaN    NaN    NaN  238.0  135.0     NaN   \n",
       "2       2785.0  155.0   NaN    NaN   NaN    NaN  238.0  238.0  122.0     NaN   \n",
       "3          NaN    NaN   NaN    NaN   NaN  391.0  220.0  234.0    NaN     NaN   \n",
       "4          NaN  132.0   6.0  300.0 -15.0    NaN  230.0    NaN  140.0  6031.0   \n",
       "...        ...    ...   ...    ...   ...    ...    ...    ...    ...     ...   \n",
       "581006     NaN    NaN   NaN    NaN   NaN  108.0    NaN    NaN  118.0   837.0   \n",
       "581007  2391.0  152.0   NaN    NaN  12.0    NaN    NaN  237.0    NaN     NaN   \n",
       "581008  2386.0    NaN  17.0    NaN   7.0    NaN  236.0    NaN  130.0     NaN   \n",
       "581009     NaN  170.0   NaN   60.0   5.0    NaN  230.0    NaN    NaN   864.0   \n",
       "581010     NaN    NaN  13.0    NaN   NaN    NaN    NaN    NaN    NaN     NaN   \n",
       "\n",
       "        ...  0.34  0.35  0.36  0.37  0.38  0.39  0.40  0.41  0.42    5  \n",
       "0       ...   NaN   0.0   0.0   NaN   NaN   0.0   NaN   0.0   0.0  5.0  \n",
       "1       ...   0.0   NaN   NaN   NaN   NaN   NaN   NaN   0.0   0.0  2.0  \n",
       "2       ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  2.0  \n",
       "3       ...   NaN   0.0   0.0   0.0   NaN   0.0   NaN   NaN   NaN  5.0  \n",
       "4       ...   NaN   0.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN  NaN  \n",
       "...     ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...  \n",
       "581006  ...   NaN   0.0   0.0   NaN   NaN   NaN   0.0   0.0   0.0  3.0  \n",
       "581007  ...   NaN   0.0   0.0   0.0   NaN   0.0   0.0   NaN   0.0  3.0  \n",
       "581008  ...   NaN   NaN   0.0   NaN   NaN   NaN   NaN   NaN   NaN  NaN  \n",
       "581009  ...   0.0   0.0   0.0   NaN   NaN   NaN   NaN   NaN   0.0  NaN  \n",
       "581010  ...   NaN   NaN   NaN   0.0   NaN   NaN   NaN   NaN   0.0  3.0  \n",
       "\n",
       "[581011 rows x 55 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('covtypeDataset/covtype.data')\n",
    "df = df.mask(np.random.choice([True, False], size=df.shape, p=[.60,.40]))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5001 55\n"
     ]
    }
   ],
   "source": [
    "table2=df.loc[0:5000]\n",
    "x,y=table2.shape\n",
    "print(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0d5094d12c94fd3b0275b690afe9f37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for k in tqdm(range(1)):\n",
    "    W=[[0.0 for i in range(x)] for j in range(x)]\n",
    "    for i in range(x):\n",
    "        for j in range(i+1,x,1):\n",
    "            z=table2[i:i+1:].append(table2[j:j+1:], ignore_index=True)\n",
    "            z=z.dropna(axis=1)\n",
    "            if z.shape[1] == 0:\n",
    "                continue\n",
    "            W[i][j]=W[j][i]=pairwise_distances(z.values,metric=\"l1\")[0][1]\n",
    "            del(z)\n",
    "#         print(i)\n",
    "print(W)\n",
    "with open(\"similarityGraph3.csv\", \"w\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerows(W)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"similarityGraph3.csv\",header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>2593.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5460.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4977.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2811.0</td>\n",
       "      <td>3121.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2629.0</td>\n",
       "      <td>2654.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2274.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>1534.0</td>\n",
       "      <td>2898.0</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>867.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 5001 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0     1      2       3       4     5     6       7       8     9     ...  \\\n",
       "0   0.0  59.0   15.0     1.0   194.0  58.0   0.0    14.0    35.0  11.0  ...   \n",
       "1  59.0   0.0  110.0  2811.0  3121.0   2.0   3.0  2629.0  2654.0  67.0  ...   \n",
       "\n",
       "     4991  4992   4993  4994   4995    4996    4997    4998    4999    5000  \n",
       "0     1.0   0.0   69.0   0.0  212.0  2593.0     0.0  5460.0     2.0  4977.0  \n",
       "1  2274.0   4.0  189.0   0.0  695.0   477.0  1534.0  2898.0  1107.0   867.0  \n",
       "\n",
       "[2 rows x 5001 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5001"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kk=df.values.tolist()\n",
    "len(kk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.000, 59.000, 15.000, ..., 3262.000, 406.000, 0.000],\n",
       "       [59.000, 0.000, 110.000, ..., 0.000, 598.000, 125.000],\n",
       "       [15.000, 110.000, 0.000, ..., 3248.000, 244.000, 145.000],\n",
       "       ...,\n",
       "       [3262.000, 0.000, 3248.000, ..., 0.000, 0.000, 0.000],\n",
       "       [406.000, 598.000, 244.000, ..., 0.000, 0.000, 346.000],\n",
       "       [0.000, 125.000, 145.000, ..., 0.000, 346.000, 0.000]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kkk=np.array(kk)\n",
    "kkk=kkk[:500,:500]\n",
    "kkk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = np.vectorize(lambda x: 1 if x < 2000 else 0)\n",
    "\n",
    "Ww = np.vectorize(vectorizer)(kk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "degree matrix:\n",
      "[[3102    0    0 ...    0    0    0]\n",
      " [   0 3739    0 ...    0    0    0]\n",
      " [   0    0 2418 ...    0    0    0]\n",
      " ...\n",
      " [   0    0    0 ... 4297    0    0]\n",
      " [   0    0    0 ...    0 4501    0]\n",
      " [   0    0    0 ...    0    0 3858]]\n",
      "laplacian matrix:\n",
      "[[3101   -1   -1 ...    0   -1    0]\n",
      " [  -1 3738   -1 ...    0   -1   -1]\n",
      " [  -1   -1 2417 ...    0   -1    0]\n",
      " ...\n",
      " [   0    0    0 ... 4296   -1    0]\n",
      " [  -1   -1   -1 ...   -1 4500   -1]\n",
      " [   0   -1    0 ...    0   -1 3857]]\n"
     ]
    }
   ],
   "source": [
    "# degree matrix\n",
    "D = np.diag(np.sum(np.array(Ww), axis=1))\n",
    "print('degree matrix:')\n",
    "print(D)\n",
    "# laplacian matrix\n",
    "L = D - Ww\n",
    "print('laplacian matrix:')\n",
    "print(L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eigenvalues:\n",
      "[-0.000 2039.761 2096.584 ... 5001.000 5001.000 5001.000]\n",
      "eigenvectors:\n",
      "[[-0.014 0.001 -0.001 ... 0.000 -0.000 0.000]\n",
      " [-0.014 -0.000 -0.000 ... -0.000 0.000 -0.000]\n",
      " [-0.014 -0.000 -0.000 ... -0.000 0.000 0.000]\n",
      " ...\n",
      " [-0.014 -0.000 0.001 ... -0.000 -0.000 0.000]\n",
      " [-0.014 -0.000 0.000 ... -0.000 0.000 -0.000]\n",
      " [-0.014 -0.000 0.001 ... 0.000 -0.000 0.000]]\n"
     ]
    }
   ],
   "source": [
    "e, v = np.linalg.eigh(L)\n",
    "# eigenvalues\n",
    "print('eigenvalues:')\n",
    "print(e)\n",
    "# eigenvectors\n",
    "print('eigenvectors:')\n",
    "print(v)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0]),)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(e<0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[18, 6])\n",
    "ax1 = plt.subplot(221)\n",
    "plt.plot(e)\n",
    "ax1.title.set_text('eigenvalues')\n",
    "i = np.where(e < 15000)[0]\n",
    "ax2 = plt.subplot(222)\n",
    "plt.plot(v[:, i[0]])\n",
    "ax3 = plt.subplot(223)\n",
    "plt.plot(v[:, i[1]])\n",
    "ax3.title.set_text('second eigenvector with eigenvalue close to 0')\n",
    "ax4 = plt.subplot(224)\n",
    "plt.plot(v[:, i[2]])\n",
    "ax4.title.set_text('third eigenvector with eigenvalue close to 0')\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "U = np.array(v[:, i[0]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 3, ..., 1, 2, 0], dtype=int32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(init='k-means++', n_clusters=4)\n",
    "km.fit(U.reshape(-1, 1))\n",
    "km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5001"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(km.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#table3=table3.iloc[:500,:]\n",
    "table3[\"Cluster\"]=km.labels_\n",
    "#table3\n",
    "\n",
    "# centroids = clusters' center points\n",
    "#centroids = cluster.cluster_centers_\n",
    "title=\"RelativeHumidity\"\n",
    "weather_clusters=table3[[\"Lat\",\"Longt\",\"Cluster\",title]]\n",
    "#print(weather_clusters)\n",
    "weather_clusters.to_csv('ClusteredDatanew.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AirTemp</th>\n",
       "      <th>Alt</th>\n",
       "      <th>DewPoint</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>RelativeHumidity</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>WindDirection</th>\n",
       "      <th>WindGust</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Longt</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>101.500000</td>\n",
       "      <td>3092.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.6900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>252.25</td>\n",
       "      <td>20.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>36.8033</td>\n",
       "      <td>-115.9772</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>3676.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193.75</td>\n",
       "      <td>20.25</td>\n",
       "      <td>12.75</td>\n",
       "      <td>36.6614</td>\n",
       "      <td>-116.0050</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>103.000000</td>\n",
       "      <td>2787.0</td>\n",
       "      <td>45.875</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>14.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193.25</td>\n",
       "      <td>16.75</td>\n",
       "      <td>9.25</td>\n",
       "      <td>36.6856</td>\n",
       "      <td>-116.4069</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>98.500000</td>\n",
       "      <td>3631.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>223.75</td>\n",
       "      <td>15.75</td>\n",
       "      <td>7.50</td>\n",
       "      <td>36.9006</td>\n",
       "      <td>-116.0428</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>102.250000</td>\n",
       "      <td>3160.0</td>\n",
       "      <td>34.675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>244.00</td>\n",
       "      <td>16.75</td>\n",
       "      <td>11.25</td>\n",
       "      <td>36.8436</td>\n",
       "      <td>-115.9083</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8512</th>\n",
       "      <td>42.533333</td>\n",
       "      <td>2949.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>47.9900</td>\n",
       "      <td>-114.0600</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8526</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>3440.0</td>\n",
       "      <td>26.300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>212.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>37.2161</td>\n",
       "      <td>-113.3778</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8528</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>42.700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.5114</td>\n",
       "      <td>-102.4969</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8561</th>\n",
       "      <td>98.000000</td>\n",
       "      <td>3180.0</td>\n",
       "      <td>34.500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>36.4367</td>\n",
       "      <td>-115.3314</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8562</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>3080.0</td>\n",
       "      <td>54.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>282.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>44.5922</td>\n",
       "      <td>-122.4278</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>740 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AirTemp     Alt  DewPoint  Pressure  RelativeHumidity  Visibility  \\\n",
       "7     101.500000  3092.0       NaN   26.6900               NaN         NaN   \n",
       "18     99.000000  3676.0       NaN   26.0725               NaN         NaN   \n",
       "20    103.000000  2787.0    45.875   27.0000             14.75         NaN   \n",
       "21     98.500000  3631.0       NaN       NaN               NaN         NaN   \n",
       "22    102.250000  3160.0    34.675       NaN              9.75         NaN   \n",
       "...          ...     ...       ...       ...               ...         ...   \n",
       "8512   42.533333  2949.0       NaN       NaN               NaN         NaN   \n",
       "8526  102.000000  3440.0    26.300       NaN              7.00         NaN   \n",
       "8528   89.000000  2610.0    42.700       NaN             20.00         NaN   \n",
       "8561   98.000000  3180.0    34.500       NaN             11.00         NaN   \n",
       "8562   63.000000  3080.0    54.600       NaN             74.00         NaN   \n",
       "\n",
       "      WindDirection  WindGust  WindSpeed      Lat     Longt  Cluster  \n",
       "7            252.25     20.00      13.00  36.8033 -115.9772        3  \n",
       "18           193.75     20.25      12.75  36.6614 -116.0050        3  \n",
       "20           193.25     16.75       9.25  36.6856 -116.4069        3  \n",
       "21           223.75     15.75       7.50  36.9006 -116.0428        3  \n",
       "22           244.00     16.75      11.25  36.8436 -115.9083        3  \n",
       "...             ...       ...        ...      ...       ...      ...  \n",
       "8512           0.00      0.00       0.00  47.9900 -114.0600        3  \n",
       "8526         212.00     18.00      10.00  37.2161 -113.3778        3  \n",
       "8528         300.00     10.00       5.00  43.5114 -102.4969        3  \n",
       "8561         137.00     16.00       7.00  36.4367 -115.3314        3  \n",
       "8562         282.00     21.00       8.00  44.5922 -122.4278        3  \n",
       "\n",
       "[740 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table4 =table2\n",
    "table4[\"Cluster\"]=km.labels_\n",
    "table4.loc[table3['Cluster'] == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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