count_target.ipynb

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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_code</th>\n",
       "      <th>target</th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_0_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-82542400</td>\n",
       "      <td>0</td>\n",
       "      <td>11.796200</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-82542399</td>\n",
       "      <td>0</td>\n",
       "      <td>5.279500</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>8.472099</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>11.644400</td>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "    ID_code  target      var_0  var_0_count\n",
       "0 -82542400       0  11.796200            5\n",
       "1 -82542399       0   5.279500            1\n",
       "2 -82542398       0   8.472099            2\n",
       "3 -82542397       1  12.271500            3\n",
       "4 -82542396       0  11.644400            4"
      ]
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     "execution_count": 2,
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   ],
   "source": [
    "var_0_count = train_gd.groupby('var_0').agg({'var_0':'count'})\n",
    "var_0_count.columns = ['var_0_count']\n",
    "var_0_count = var_0_count.reset_index()\n",
    "train_gd = train_gd.merge(var_0_count,on='var_0',how='left')\n",
    "\n",
    "train_gd.head()[['ID_code','target','var_0','var_0_count']].to_pandas()"
   ]
  }
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