{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clusters  (example 02) ##\n",
    "\n",
    "Purpose: Identify clusters of Gira docking station\n",
    "\n",
    "1 import libraries needed:numpy, sklearn, matplotlib and pandas\n",
    "\n",
    "2 generate a sample of blobs and convert it into a dataframe called df1\n",
    "\n",
    "3 Verify datatype\n",
    "\n",
    "4 Plot the blobs\n",
    "\n",
    "5 calculete WCSS\n",
    "\n",
    "6 plot the new chart with centroids\n",
    "\n",
    "7 identify to what group does each item belongs\n",
    "\n",
    "Comment the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file='https://github.com/masterfloss/data/blob/main/giras201030.csv?raw=true'\n",
    "dfGiras=pd.read_csv(file,sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>desigcomercial</th>\n",
       "      <th>numbicicletas</th>\n",
       "      <th>numdocasvacias</th>\n",
       "      <th>position</th>\n",
       "      <th>add_data</th>\n",
       "      <th>add_hora</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>206 - Av. Brasília / Doca de Alcântara</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>{\"coordinates\": [-9.175, 38.701], \"type\": \"Poi...</td>\n",
       "      <td>2019-07-30</td>\n",
       "      <td>15:07:34.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110 - Rua de Moscavide</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>{\"coordinates\": [-9.097068, 38.778466], \"type\"...</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>19:49:28.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>208 - Cais das Pombas</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>{\"coordinates\": [-9.14373, 38.70523], \"type\": ...</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>19:49:29.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>415 - Av. Duque de Ávila / Av. Conde Valbom</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>{\"coordinates\": [-9.148443, 38.735188], \"type\"...</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>19:49:29.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>423 - Av. Conde Valbom / Rua Marquês Sá da Ban...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>{\"coordinates\": [-9.152295, 38.737911], \"type\"...</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>19:49:29.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      desigcomercial  numbicicletas  \\\n",
       "0             206 - Av. Brasília / Doca de Alcântara              2   \n",
       "1                             110 - Rua de Moscavide              5   \n",
       "2                              208 - Cais das Pombas             11   \n",
       "3        415 - Av. Duque de Ávila / Av. Conde Valbom              2   \n",
       "4  423 - Av. Conde Valbom / Rua Marquês Sá da Ban...              5   \n",
       "\n",
       "   numdocasvacias                                           position  \\\n",
       "0              20  {\"coordinates\": [-9.175, 38.701], \"type\": \"Poi...   \n",
       "1              13  {\"coordinates\": [-9.097068, 38.778466], \"type\"...   \n",
       "2              11  {\"coordinates\": [-9.14373, 38.70523], \"type\": ...   \n",
       "3              18  {\"coordinates\": [-9.148443, 38.735188], \"type\"...   \n",
       "4               5  {\"coordinates\": [-9.152295, 38.737911], \"type\"...   \n",
       "\n",
       "     add_data   add_hora  \n",
       "0  2019-07-30  15:07:34.  \n",
       "1  2020-10-30  19:49:28.  \n",
       "2  2020-10-30  19:49:29.  \n",
       "3  2020-10-30  19:49:29.  \n",
       "4  2020-10-30  19:49:29.  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfGiras.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'-9.175'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfGiras.loc[0,'position'].split()[1].replace('[','').replace(',','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(dfGiras['position'])):\n",
    "    dfGiras.loc[i,'long']=dfGiras.loc[i,'position'].split()[1].replace('[','').replace(',','')\n",
    "    dfGiras.loc[i,'lat']=dfGiras.loc[i,'position'].split()[2].replace('],','')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>desigcomercial</th>\n",
       "      <th>numbicicletas</th>\n",
       "      <th>numdocasvacias</th>\n",
       "      <th>position</th>\n",
       "      <th>add_data</th>\n",
       "      <th>add_hora</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>206 - Av. Brasília / Doca de Alcântara</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>{\"coordinates\": [-9.175, 38.701], \"type\": \"Poi...</td>\n",
       "      <td>2019-07-30</td>\n",
       "      <td>15:07:34.</td>\n",
       "      <td>-9.175</td>\n",
       "      <td>38.701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110 - Rua de Moscavide</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>{\"coordinates\": [-9.097068, 38.778466], \"type\"...</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>19:49:28.</td>\n",
       "      <td>-9.097068</td>\n",
       "      <td>38.778466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>208 - Cais das Pombas</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>{\"coordinates\": [-9.14373, 38.70523], \"type\": ...</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>19:49:29.</td>\n",
       "      <td>-9.14373</td>\n",
       "      <td>38.70523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>415 - Av. Duque de Ávila / Av. Conde Valbom</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>{\"coordinates\": [-9.148443, 38.735188], \"type\"...</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>19:49:29.</td>\n",
       "      <td>-9.148443</td>\n",
       "      <td>38.735188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>423 - Av. Conde Valbom / Rua Marquês Sá da Ban...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>{\"coordinates\": [-9.152295, 38.737911], \"type\"...</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>19:49:29.</td>\n",
       "      <td>-9.152295</td>\n",
       "      <td>38.737911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      desigcomercial  numbicicletas  \\\n",
       "0             206 - Av. Brasília / Doca de Alcântara              2   \n",
       "1                             110 - Rua de Moscavide              5   \n",
       "2                              208 - Cais das Pombas             11   \n",
       "3        415 - Av. Duque de Ávila / Av. Conde Valbom              2   \n",
       "4  423 - Av. Conde Valbom / Rua Marquês Sá da Ban...              5   \n",
       "\n",
       "   numdocasvacias                                           position  \\\n",
       "0              20  {\"coordinates\": [-9.175, 38.701], \"type\": \"Poi...   \n",
       "1              13  {\"coordinates\": [-9.097068, 38.778466], \"type\"...   \n",
       "2              11  {\"coordinates\": [-9.14373, 38.70523], \"type\": ...   \n",
       "3              18  {\"coordinates\": [-9.148443, 38.735188], \"type\"...   \n",
       "4               5  {\"coordinates\": [-9.152295, 38.737911], \"type\"...   \n",
       "\n",
       "     add_data   add_hora       long        lat  \n",
       "0  2019-07-30  15:07:34.     -9.175     38.701  \n",
       "1  2020-10-30  19:49:28.  -9.097068  38.778466  \n",
       "2  2020-10-30  19:49:29.   -9.14373   38.70523  \n",
       "3  2020-10-30  19:49:29.  -9.148443  38.735188  \n",
       "4  2020-10-30  19:49:29.  -9.152295  38.737911  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfGiras.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "long    object\n",
       "lat     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=dfGiras[['long','lat']]\n",
    "\n",
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1047: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item_labels[indexer[info_axis]]] = value\n",
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:965: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "df1.loc[:,'long']=pd.to_numeric(df1.loc[:,'long'])\n",
    "df1.loc[:,'lat']=pd.to_numeric(df1.loc[:,'lat'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "long    float64\n",
       "lat     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'lat')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df1['long'], df1['lat'])\n",
    "plt.title('Giras')\n",
    "plt.xlabel('long')\n",
    "plt.ylabel('lat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    model = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "    model.fit(df1)\n",
    "    wcss.append(model.inertia_)\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model1 = KMeans(n_clusters=5, init='k-means++', max_iter=400, n_init=10, random_state=0)\n",
    "model1.fit_predict(df1)\n",
    "plt.scatter(df1[\"long\"], df1[\"lat\"])\n",
    "plt.scatter(model1.cluster_centers_[:, 0], model1.cluster_centers_[:, 1], s=300, c='red')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.predict(df1.loc[0:0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.predict(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}