corr_numpy_pandas_scipy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>COD_PROD_01</th>\n",
       "      <th>COD_PROD_02</th>\n",
       "      <th>COD_PROD_03</th>\n",
       "      <th>COD_PROD_04</th>\n",
       "      <th>COD_PROD_05</th>\n",
       "      <th>COD_PROD_06</th>\n",
       "      <th>TOTAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COD_PROD_01</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002795</td>\n",
       "      <td>-0.007691</td>\n",
       "      <td>-0.003901</td>\n",
       "      <td>-0.017263</td>\n",
       "      <td>-0.002304</td>\n",
       "      <td>0.000831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_PROD_02</th>\n",
       "      <td>-0.002795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009383</td>\n",
       "      <td>-0.004759</td>\n",
       "      <td>-0.021062</td>\n",
       "      <td>-0.002811</td>\n",
       "      <td>-0.003440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_PROD_03</th>\n",
       "      <td>-0.007691</td>\n",
       "      <td>-0.009383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.013097</td>\n",
       "      <td>-0.057958</td>\n",
       "      <td>-0.007735</td>\n",
       "      <td>-0.007493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_PROD_04</th>\n",
       "      <td>-0.003901</td>\n",
       "      <td>-0.004759</td>\n",
       "      <td>-0.013097</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.029398</td>\n",
       "      <td>-0.003923</td>\n",
       "      <td>0.055012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_PROD_05</th>\n",
       "      <td>-0.017263</td>\n",
       "      <td>-0.021062</td>\n",
       "      <td>-0.057958</td>\n",
       "      <td>-0.029398</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.017361</td>\n",
       "      <td>0.032142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_PROD_06</th>\n",
       "      <td>-0.002304</td>\n",
       "      <td>-0.002811</td>\n",
       "      <td>-0.007735</td>\n",
       "      <td>-0.003923</td>\n",
       "      <td>-0.017361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>0.000831</td>\n",
       "      <td>-0.003440</td>\n",
       "      <td>-0.007493</td>\n",
       "      <td>0.055012</td>\n",
       "      <td>0.032142</td>\n",
       "      <td>0.996930</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             COD_PROD_01  COD_PROD_02  COD_PROD_03  COD_PROD_04  COD_PROD_05  \\\n",
       "COD_PROD_01     1.000000    -0.002795    -0.007691    -0.003901    -0.017263   \n",
       "COD_PROD_02    -0.002795     1.000000    -0.009383    -0.004759    -0.021062   \n",
       "COD_PROD_03    -0.007691    -0.009383     1.000000    -0.013097    -0.057958   \n",
       "COD_PROD_04    -0.003901    -0.004759    -0.013097     1.000000    -0.029398   \n",
       "COD_PROD_05    -0.017263    -0.021062    -0.057958    -0.029398     1.000000   \n",
       "COD_PROD_06    -0.002304    -0.002811    -0.007735    -0.003923    -0.017361   \n",
       "TOTAL           0.000831    -0.003440    -0.007493     0.055012     0.032142   \n",
       "\n",
       "             COD_PROD_06     TOTAL  \n",
       "COD_PROD_01    -0.002304  0.000831  \n",
       "COD_PROD_02    -0.002811 -0.003440  \n",
       "COD_PROD_03    -0.007735 -0.007493  \n",
       "COD_PROD_04    -0.003923  0.055012  \n",
       "COD_PROD_05    -0.017361  0.032142  \n",
       "COD_PROD_06     1.000000  0.996930  \n",
       "TOTAL           0.996930  1.000000  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlación Pearson con pandas.\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_excel('C:\\\\Users\\\\Intel\\\\Documents\\\\Mis documentos IDEA\\\\Samples\\\\Exportaciones.ILB\\\\Tabladinámica10.xlsx')\n",
    "df.head()\n",
    "del df['NUM_FACT']\n",
    "df.head()\n",
    "df.corr(method='pearson')\n",
    "\n",
    "#plt.plot(df['COD_PROD_06'], df['TOTAL'], \"ro\")\n",
    "#plt.xlabel('Producto 06')\n",
    "#plt.ylabel('Ventas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.99693027]\n",
      " [0.99693027 1.        ]]\n",
      "[[1.         0.03214211]\n",
      " [0.03214211 1.        ]]\n",
      "[[1.         0.05501175]\n",
      " [0.05501175 1.        ]]\n",
      "[[ 1.        -0.0074928]\n",
      " [-0.0074928  1.       ]]\n",
      "[[ 1.         -0.00344006]\n",
      " [-0.00344006  1.        ]]\n",
      "[[1.00000000e+00 8.30539999e-04]\n",
      " [8.30539999e-04 1.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "#Correlación Pearson con numpy\n",
    "import numpy as np\n",
    "\n",
    "CORR6 = np.corrcoef(df['COD_PROD_06'],df['TOTAL'])\n",
    "CORR5 = np.corrcoef(df['COD_PROD_05'],df['TOTAL'])\n",
    "CORR4 = np.corrcoef(df['COD_PROD_04'],df['TOTAL'])\n",
    "CORR3 = np.corrcoef(df['COD_PROD_03'],df['TOTAL'])\n",
    "CORR2 = np.corrcoef(df['COD_PROD_02'],df['TOTAL'])\n",
    "CORR1 = np.corrcoef(df['COD_PROD_01'],df['TOTAL'])\n",
    "\n",
    "print(CORR6)\n",
    "print(CORR5)\n",
    "print(CORR4)\n",
    "print(CORR3)\n",
    "print(CORR2)\n",
    "print(CORR1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.996930270131928, 0.0)\n",
      "(0.032142112200437334, 0.3370817866001227)\n",
      "(0.05501175102730851, 0.10022047614296177)\n",
      "(-0.007492802890192813, 0.822972656588921)\n",
      "(-0.00344005940841924, 0.9181902823917176)\n",
      "(0.000830539999422464, 0.9802158731696501)\n"
     ]
    }
   ],
   "source": [
    "#Correlación Pearson con scipy\n",
    "from scipy import stats\n",
    "\n",
    "c6 = stats.pearsonr(df['COD_PROD_06'],df['TOTAL'])\n",
    "c5 = stats.pearsonr(df['COD_PROD_05'],df['TOTAL'])\n",
    "c4 = stats.pearsonr(df['COD_PROD_04'],df['TOTAL'])\n",
    "c3 = stats.pearsonr(df['COD_PROD_03'],df['TOTAL'])\n",
    "c2 = stats.pearsonr(df['COD_PROD_02'],df['TOTAL'])\n",
    "c1 = stats.pearsonr(df['COD_PROD_01'],df['TOTAL'])\n",
    "print(c6)\n",
    "print(c5)\n",
    "print(c4)\n",
    "print(c3)\n",
    "print(c2)\n",
    "print(c1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COD_PROD_01</th>\n",
       "      <th>COD_PROD_02</th>\n",
       "      <th>COD_PROD_03</th>\n",
       "      <th>COD_PROD_04</th>\n",
       "      <th>COD_PROD_05</th>\n",
       "      <th>COD_PROD_06</th>\n",
       "      <th>TOTAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COD_PROD_01</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003890</td>\n",
       "      <td>-0.012222</td>\n",
       "      <td>-0.007833</td>\n",
       "      <td>-0.092657</td>\n",
       "      <td>-0.003367</td>\n",
       "      <td>0.077556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_PROD_02</th>\n",
       "      <td>-0.003890</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014121</td>\n",
       "      <td>-0.009049</td>\n",
       "      <td>-0.107051</td>\n",
       "      <td>-0.003890</td>\n",
       "      <td>0.007838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_PROD_03</th>\n",
       "      <td>-0.012222</td>\n",
       "      <td>-0.014121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028432</td>\n",
       "      <td>-0.336336</td>\n",
       "      <td>-0.012222</td>\n",
       "      <td>0.083242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_PROD_04</th>\n",
       "      <td>-0.007833</td>\n",
       "      <td>-0.009049</td>\n",
       "      <td>-0.028432</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.215548</td>\n",
       "      <td>-0.007833</td>\n",
       "      <td>0.199820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_PROD_05</th>\n",
       "      <td>-0.092657</td>\n",
       "      <td>-0.107051</td>\n",
       "      <td>-0.336336</td>\n",
       "      <td>-0.215548</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.092657</td>\n",
       "      <td>0.762715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_PROD_06</th>\n",
       "      <td>-0.003367</td>\n",
       "      <td>-0.003890</td>\n",
       "      <td>-0.012222</td>\n",
       "      <td>-0.007833</td>\n",
       "      <td>-0.092657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>0.077556</td>\n",
       "      <td>0.007838</td>\n",
       "      <td>0.083242</td>\n",
       "      <td>0.199820</td>\n",
       "      <td>0.762715</td>\n",
       "      <td>0.100094</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             COD_PROD_01  COD_PROD_02  COD_PROD_03  COD_PROD_04  COD_PROD_05  \\\n",
       "COD_PROD_01     1.000000    -0.003890    -0.012222    -0.007833    -0.092657   \n",
       "COD_PROD_02    -0.003890     1.000000    -0.014121    -0.009049    -0.107051   \n",
       "COD_PROD_03    -0.012222    -0.014121     1.000000    -0.028432    -0.336336   \n",
       "COD_PROD_04    -0.007833    -0.009049    -0.028432     1.000000    -0.215548   \n",
       "COD_PROD_05    -0.092657    -0.107051    -0.336336    -0.215548     1.000000   \n",
       "COD_PROD_06    -0.003367    -0.003890    -0.012222    -0.007833    -0.092657   \n",
       "TOTAL           0.077556     0.007838     0.083242     0.199820     0.762715   \n",
       "\n",
       "             COD_PROD_06     TOTAL  \n",
       "COD_PROD_01    -0.003367  0.077556  \n",
       "COD_PROD_02    -0.003890  0.007838  \n",
       "COD_PROD_03    -0.012222  0.083242  \n",
       "COD_PROD_04    -0.007833  0.199820  \n",
       "COD_PROD_05    -0.092657  0.762715  \n",
       "COD_PROD_06     1.000000  0.100094  \n",
       "TOTAL           0.100094  1.000000  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlación Spearman con pandas\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_excel('C:\\\\Users\\\\Intel\\\\Documents\\\\Mis documentos IDEA\\\\Samples\\\\Exportaciones.ILB\\\\Tabladinámica10.xlsx')\n",
    "df.head()\n",
    "del df['NUM_FACT']\n",
    "df.head()\n",
    "df.corr(method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpearmanrResult(correlation=0.10009409823426017, pvalue=0.002734237934284886)\n",
      "SpearmanrResult(correlation=0.7627145125422655, pvalue=5.152684052961035e-171)\n",
      "SpearmanrResult(correlation=0.1998197051934975, pvalue=1.6692693298847711e-09)\n",
      "SpearmanrResult(correlation=0.08324199826137126, pvalue=0.012782875777367107)\n",
      "SpearmanrResult(correlation=0.007837615227853811, pvalue=0.8149701455508731)\n",
      "SpearmanrResult(correlation=0.07755642532633315, pvalue=0.020385623258571062)\n"
     ]
    }
   ],
   "source": [
    "#Correlación Spearman con scipy\n",
    "from scipy import stats\n",
    "\n",
    "C6 = stats.spearmanr(df['COD_PROD_06'],df['TOTAL'])\n",
    "C5 = stats.spearmanr(df['COD_PROD_05'],df['TOTAL'])\n",
    "C4 = stats.spearmanr(df['COD_PROD_04'],df['TOTAL'])\n",
    "C3 = stats.spearmanr(df['COD_PROD_03'],df['TOTAL'])\n",
    "C2 = stats.spearmanr(df['COD_PROD_02'],df['TOTAL'])\n",
    "C1 = stats.spearmanr(df['COD_PROD_01'],df['TOTAL'])\n",
    "print(C6)\n",
    "print(C5)\n",
    "print(C4)\n",
    "print(C3)\n",
    "print(C2)\n",
    "print(C1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KendalltauResult(correlation=0.08195162614371349, pvalue=0.0027492645317029776)\n",
      "KendalltauResult(correlation=0.8028435716684932, pvalue=1.57185287952293e-279)\n",
      "KendalltauResult(correlation=0.16412175697057554, pvalue=1.7576235038473602e-09)\n",
      "KendalltauResult(correlation=0.07063439714722591, pvalue=0.009156887797044266)\n",
      "KendalltauResult(correlation=0.006477961821423651, pvalue=0.8128374588931795)\n",
      "KendalltauResult(correlation=0.06350178360874134, pvalue=0.02032365124093628)\n"
     ]
    }
   ],
   "source": [
    "#Correlación Kendall con scipy\n",
    "import scipy.stats\n",
    "from scipy import stats\n",
    "\n",
    "C6 = scipy.stats.kendalltau(df['COD_PROD_06'],df['TOTAL'])\n",
    "C5 = scipy.stats.kendalltau(df['COD_PROD_05'],df['TOTAL'])\n",
    "C4 = scipy.stats.kendalltau(df['COD_PROD_04'],df['TOTAL'])\n",
    "C3 = scipy.stats.kendalltau(df['COD_PROD_03'],df['TOTAL'])\n",
    "C2 = scipy.stats.kendalltau(df['COD_PROD_02'],df['TOTAL'])\n",
    "C1 = scipy.stats.kendalltau(df['COD_PROD_01'],df['TOTAL'])\n",
    "\n",
    "print(C6)\n",
    "print(C5)\n",
    "print(C4)\n",
    "print(C3)\n",
    "print(C2)\n",
    "print(C1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>CI</th>\n",
       "      <th>TV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CI</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.213632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>-0.213632</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CI        TV\n",
       "CI  1.000000 -0.213632\n",
       "TV -0.213632  1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set de pruebas Correlación Spearman con pandas y scipy\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_excel('C:\\\\Users\\\\Intel\\\\Documents\\\\Mis documentos IDEA\\\\Samples\\\\Archivos fuente.ILB\\\\Spearman.xlsx')\n",
    "df.corr(method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpearmanrResult(correlation=-0.21363150574154266, pvalue=0.5534311070541142)\n"
     ]
    }
   ],
   "source": [
    "#Correlación Spearman con scipy\n",
    "from scipy import stats\n",
    "\n",
    "C6 = stats.spearmanr(df['CI'],df['TV'])\n",
    "print(C6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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