weather_diagram.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9d0e9ed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b52e72ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster_index</th>\n",
       "      <th>air_pressure</th>\n",
       "      <th>air_temp</th>\n",
       "      <th>relative_humidity</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1587257 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         cluster_index  air_pressure  air_temp  relative_humidity\n",
       "0                    0         912.3     64.76               60.5\n",
       "1                    0         912.3     63.86               39.9\n",
       "2                    0         912.3     64.22               43.0\n",
       "3                    0         912.3     64.40               49.5\n",
       "4                    0         912.3     64.40               58.8\n",
       "...                ...           ...       ...                ...\n",
       "1587252              0         915.9     74.66               46.3\n",
       "1587253              0         915.9     74.66               46.2\n",
       "1587254              0         915.9     74.66               46.3\n",
       "1587255              0         915.9     74.66               46.2\n",
       "1587256              0         915.9     74.66               46.3\n",
       "\n",
       "[1587257 rows x 4 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather = pd.read_csv('./r1_weather_pointMapCluster/part-00000-e50c8ded-62bd-4273-95c0-bb24589b34f6-c000.csv')\n",
    "weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a323f481",
   "metadata": {},
   "outputs": [],
   "source": [
    "# weather = weather.sample(frac=0.5)\n",
    "pca_weather = PCA(n_components=2)\n",
    "pcaComponent_weather = pca_weather.fit_transform(weather.iloc[:, 1:].values)\n",
    "labels = weather.iloc[:, 0]\n",
    "\n",
    "pcaComponent_weather_DF = pd.DataFrame(data = pcaComponent_weather, columns = ['pc1', 'pc2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3a67833f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          0\n",
       "1          0\n",
       "2          0\n",
       "3          0\n",
       "4          0\n",
       "          ..\n",
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       "1587253    0\n",
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       "Name: cluster_index, Length: 1587257, dtype: int64"
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     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d8329c30",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>pc1</th>\n",
       "      <th>pc2</th>\n",
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      "text/plain": [
       "              pc1        pc2\n",
       "1587252 -5.065776 -11.872857\n",
       "1587253 -5.161056 -11.843279\n",
       "1587254 -5.065776 -11.872857\n",
       "1587255 -5.161056 -11.843279\n",
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     "metadata": {},
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   ],
   "source": [
    "pcaComponent_weather_DF.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c241f48a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f98c9641100>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xlabel('Principle Component -1', fontsize=20)\n",
    "plt.ylabel('Principle Component -2', fontsize=20)\n",
    "plt.title(\"Principle Component Analysis of Weather\", fontsize=20)\n",
    "targets = [0, 1, 2]\n",
    "colors = ['r', 'g', 'b']\n",
    "\n",
    "for target, color in zip(targets, colors):\n",
    "    indicesToKeep = weather['cluster_index'] == target\n",
    "    plt.scatter(pcaComponent_weather_DF.loc[indicesToKeep, 'pc1'],\n",
    "               pcaComponent_weather_DF.loc[indicesToKeep, 'pc2'],\n",
    "               c= color,\n",
    "               s = 50)\n",
    "plt.legend(targets, prop={'size':15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7db675b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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