T3-1-Statistics-Correlación.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('../GitHub/python-ml-course/datasets/ads/Advertising.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
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       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
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       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>200 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales\n",
       "0    230.1   37.8       69.2   22.1\n",
       "1     44.5   39.3       45.1   10.4\n",
       "2     17.2   45.9       69.3    9.3\n",
       "3    151.5   41.3       58.5   18.5\n",
       "4    180.8   10.8       58.4   12.9\n",
       "..     ...    ...        ...    ...\n",
       "195   38.2    3.7       13.8    7.6\n",
       "196   94.2    4.9        8.1    9.7\n",
       "197  177.0    9.3        6.4   12.8\n",
       "198  283.6   42.0       66.2   25.5\n",
       "199  232.1    8.6        8.7   13.4\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['corrn']=(data.TV-np.mean(data.TV))*(data.Sales-np.mean(data.Sales))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "      <th>corrn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "      <td>670.896956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "      <td>371.460206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>613.181206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>19.958456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "      <td>-37.892794</td>\n",
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       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>699.040956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "      <td>228.411706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "      <td>-36.623044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1567.338706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "      <td>-52.948294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales        corrn\n",
       "0    230.1   37.8       69.2   22.1   670.896956\n",
       "1     44.5   39.3       45.1   10.4   371.460206\n",
       "2     17.2   45.9       69.3    9.3   613.181206\n",
       "3    151.5   41.3       58.5   18.5    19.958456\n",
       "4    180.8   10.8       58.4   12.9   -37.892794\n",
       "..     ...    ...        ...    ...          ...\n",
       "195   38.2    3.7       13.8    7.6   699.040956\n",
       "196   94.2    4.9        8.1    9.7   228.411706\n",
       "197  177.0    9.3        6.4   12.8   -36.623044\n",
       "198  283.6   42.0       66.2   25.5  1567.338706\n",
       "199  232.1    8.6        8.7   13.4   -52.948294\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['corr1']=(data.TV-np.mean(data.TV))**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['corr2']=(data.Sales-np.mean(data.Sales))**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_pearson=sum(data.corrn)/np.sqrt((sum(data.corr1)*sum(data.corr2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.782224424861606"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_pearson"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede crear una función que calcule dicho coeficiente!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,[ax1,ax2,ax3]=plt.subplots(1,3,figsize=(12,5))\n",
    "ax1.plot(data.TV,data.Sales,'ro')\n",
    "ax2.plot(data.Radio,data.Sales,'bo')\n",
    "ax3.plot(data.Newspaper,data.Sales,'go')\n",
    "plt.subplots_adjust(wspace=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054809</td>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.782224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radio</th>\n",
       "      <td>0.054809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>0.576223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newspaper</th>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.228299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>0.782224</td>\n",
       "      <td>0.576223</td>\n",
       "      <td>0.228299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TV     Radio  Newspaper     Sales\n",
       "TV         1.000000  0.054809   0.056648  0.782224\n",
       "Radio      0.054809  1.000000   0.354104  0.576223\n",
       "Newspaper  0.056648  0.354104   1.000000  0.228299\n",
       "Sales      0.782224  0.576223   0.228299  1.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fddac5b5278>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/bJI9opRu53qSPaKUNh9M0USSPaKUJHtEBWp9I8xLB14+et/cQ/8zhabXAEen0C7w0HSanVLMz5du8Oem9m88c9E0WgWmF/PE20Z/rtJXNtu+cBrtSto9YZth5/Qt5r7FCx2KucZkj6iOgfluT8cn2SOKMDjJXtJp3QbYsr7F3Ld4oSsxZxhfzune89umvsXct3ihIzHXOhsfUaX07BGVSLJHVMCG+fm2oxgryR5RSnr2iEok2SNq0O4bWptIskeUYHA21URUIj17RCVyzR5RgSy9RdTDeeBkRA0qfXhFRHVyI0xERbL0FrH8GXB69ogKOE+qiaiGO770Jnd8BjGiDyT9C4NHWjdx1PamacZzMkn2iEqsaDuAiDgzkuwRlUiyR1QiyR5RiSR7RCX+D5iTTMDUreusAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(data.corr())\n",
    "plt.colorbar()"
   ]
  },
  {
   "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": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}