categ_analysis.ipynb

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     "text": [
      "Cramers V:  0.39198396239045336 \n",
      " Moderate association.\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>carlength</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>carbody</th>\n",
       "      <th>cylindernumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>symboling</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.391984</td>\n",
       "      <td>0.281235</td>\n",
       "      <td>0.382220</td>\n",
       "      <td>0.247163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carlength</th>\n",
       "      <td>0.391984</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.192294</td>\n",
       "      <td>0.304682</td>\n",
       "      <td>0.337011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aspiration</th>\n",
       "      <td>0.281235</td>\n",
       "      <td>0.192294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140674</td>\n",
       "      <td>0.126887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carbody</th>\n",
       "      <td>0.382220</td>\n",
       "      <td>0.304682</td>\n",
       "      <td>0.140674</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylindernumber</th>\n",
       "      <td>0.247163</td>\n",
       "      <td>0.337011</td>\n",
       "      <td>0.126887</td>\n",
       "      <td>0.129380</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                symboling  carlength  aspiration   carbody  cylindernumber\n",
       "symboling        1.000000   0.391984    0.281235  0.382220        0.247163\n",
       "carlength        0.391984   1.000000    0.192294  0.304682        0.337011\n",
       "aspiration       0.281235   0.192294    1.000000  0.140674        0.126887\n",
       "carbody          0.382220   0.304682    0.140674  1.000000        0.129380\n",
       "cylindernumber   0.247163   0.337011    0.126887  0.129380        1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "df = pd.read_excel('./Data/car_data_selection.xlsx')\n",
    "\n",
    "cat_cols = [\"symboling\",\"carlength\",\"aspiration\",\"carbody\",\"cylindernumber\"]\n",
    "df[cat_cols] = df[cat_cols].astype('category')\n",
    "\n",
    "categ_variables = df[cat_cols]\n",
    "\n",
    "#Create the contingency table\n",
    "data_crosstab = pd.crosstab(categ_variables['symboling'],\n",
    "                            categ_variables['carlength'], \n",
    "                               margins = False)\n",
    "\n",
    "#Cramer V test\n",
    "def cramers_corrected_stat(confusion_matrix):\n",
    "    \"\"\" calculate Cramers V statistic for categorial-categorial association.\n",
    "    \"\"\"\n",
    "    chi2 = stats.chi2_contingency(confusion_matrix)[0]\n",
    "    n = sum(confusion_matrix.sum())\n",
    "    return np.sqrt(chi2 / (n*(min(confusion_matrix.shape)-1)))\n",
    "\n",
    "V = cramers_corrected_stat(data_crosstab)\n",
    "\n",
    "association_level = ''\n",
    "if(V<0.1):\n",
    "    association_level='Little, if any association.'\n",
    "else:\n",
    "    if(V<0.3):\n",
    "        association_level=\"Low association.\"\n",
    "    else:\n",
    "        if(V<0.5):\n",
    "            association_level=\"Moderate association.\"\n",
    "        else:\n",
    "            association_level=\"High association\"\n",
    "print('Cramers V: ', V, '\\n', association_level)\n",
    "\n",
    "import association_metrics as am\n",
    "\n",
    "#Cramer V matrix for all categorical variables\n",
    "cramersv = am.CramersV(categ_variables)\n",
    "cramersv.fit()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#\n"
   ]
  }
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