{
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   "cell_type": "markdown",
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   "source": [
    "#### Author：马肖\n",
    "#### E-Mail：maxiaoscut@aliyun.com\n",
    "#### GitHub：https://github.com/Albertsr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "def high_categorical(dataframe, high_discrete, k=3):\n",
    "    # df为pandas.DataFrame格式\n",
    "    # feature为df的某一列高势集离散型特征，为pandas.Series格式\n",
    "    # k表示上述离散型特征出现频次最高的k个不重复取值\n",
    "    \n",
    "    value_counts = high_discrete.value_counts()\n",
    "    top_categories = list(value_counts[:k].index)\n",
    "    top_categories.append('other')\n",
    "    \n",
    "    high_discrete = high_discrete.apply(lambda category: category if category in top_categories else 'other')\n",
    "    #print(high_discrete)\n",
    "    feature_dummies = pd.get_dummies(high_discrete, prefix=high_discrete.name)\n",
    "    \n",
    "    dataframe = dataframe.join(feature_dummies)\n",
    "    dataframe.drop(high_discrete.name, axis=1, inplace=True)\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>销售额</th>\n",
       "      <th>邮编</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>142</td>\n",
       "      <td>10072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>140</td>\n",
       "      <td>10114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>130</td>\n",
       "      <td>10037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>10024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>136</td>\n",
       "      <td>10029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   销售额     邮编\n",
       "0  142  10072\n",
       "1  140  10114\n",
       "2  130  10037\n",
       "3  108  10024\n",
       "4  136  10029"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2019)\n",
    "zipcode = np.random.randint(10000, 10150, size=5000)\n",
    "sales = np.random.randint(100, 150, size=5000)\n",
    "df = pd.DataFrame({'销售额':sales, '邮编':zipcode})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>销售额</th>\n",
       "      <th>邮编_10001</th>\n",
       "      <th>邮编_10012</th>\n",
       "      <th>邮编_10075</th>\n",
       "      <th>邮编_10114</th>\n",
       "      <th>邮编_10126</th>\n",
       "      <th>邮编_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   销售额  邮编_10001  邮编_10012  邮编_10075  邮编_10114  邮编_10126  邮编_other\n",
       "0  142         0         0         0         0         0         1\n",
       "1  140         0         0         0         1         0         0\n",
       "2  130         0         0         0         0         0         1\n",
       "3  108         0         0         0         0         0         1\n",
       "4  136         0         0         0         0         0         1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_categorical(df, df['邮编'], k=5).head(5)"
   ]
  }
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