22. Pandas怎么处理日期索引的缺失.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas怎么处理日期索引的缺失？\n",
    "\n",
    "\n",
    "问题：按日期统计的数据，缺失了某天，导致数据不全该怎么补充日期？\n",
    "\n",
    "公众号：蚂蚁学Python\n",
    "\n",
    "可以用两种方法实现：  \n",
    "1、DataFrame.reindex，调整dataframe的索引以适应新的索引  \n",
    "2、DataFrame.resample，可以对时间序列重采样，支持补充缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问题：如果缺失了索引该怎么填充？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "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>pdate</th>\n",
       "      <th>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>200</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>400</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>500</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pdate   pv  uv\n",
       "0  2019-12-01  100  10\n",
       "1  2019-12-02  200  20\n",
       "2  2019-12-04  400  40\n",
       "3  2019-12-05  500  50"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    \"pdate\": [\"2019-12-01\", \"2019-12-02\", \"2019-12-04\", \"2019-12-05\"],\n",
    "    \"pv\": [100, 200, 400, 500],\n",
    "    \"uv\": [10, 20, 40, 50],\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe91cdbaf0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index(\"pdate\").plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***问题，这里缺失了2019-12-03的数据，导致数据不全该怎么补充？***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 方法1：使用pandas.reindex方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、将df的索引变成日期索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-02</th>\n",
       "      <td>200</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-04</th>\n",
       "      <td>400</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-05</th>\n",
       "      <td>500</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pv  uv\n",
       "pdate              \n",
       "2019-12-01  100  10\n",
       "2019-12-02  200  20\n",
       "2019-12-04  400  40\n",
       "2019-12-05  500  50"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date = df.set_index(\"pdate\")\n",
    "df_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2019-12-01', '2019-12-02', '2019-12-04', '2019-12-05'], dtype='object', name='pdate')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-02</th>\n",
       "      <td>200</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-04</th>\n",
       "      <td>400</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-05</th>\n",
       "      <td>500</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pv  uv\n",
       "pdate              \n",
       "2019-12-01  100  10\n",
       "2019-12-02  200  20\n",
       "2019-12-04  400  40\n",
       "2019-12-05  500  50"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将df的索引设置为日期索引\n",
    "df_date = df_date.set_index(pd.to_datetime(df_date.index))\n",
    "df_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-12-01', '2019-12-02', '2019-12-04', '2019-12-05'], dtype='datetime64[ns]', name='pdate', freq=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、使用pandas.reindex填充缺失的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-12-01', '2019-12-02', '2019-12-03', '2019-12-04',\n",
       "               '2019-12-05'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成完整的日期序列\n",
    "pdates = pd.date_range(start=\"2019-12-01\", end=\"2019-12-05\")\n",
    "pdates"
   ]
  },
  {
   "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",
       "    .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>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-02</th>\n",
       "      <td>200</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-03</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-04</th>\n",
       "      <td>400</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-05</th>\n",
       "      <td>500</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pv  uv\n",
       "2019-12-01  100  10\n",
       "2019-12-02  200  20\n",
       "2019-12-03    0   0\n",
       "2019-12-04  400  40\n",
       "2019-12-05  500  50"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date_new = df_date.reindex(pdates, fill_value=0)\n",
    "df_date_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe91cdbd90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_date_new.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 方法2：使用pandas.resample方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、先将索引变成日期索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>pdate</th>\n",
       "      <th>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>200</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>400</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>500</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pdate   pv  uv\n",
       "0  2019-12-01  100  10\n",
       "1  2019-12-02  200  20\n",
       "2  2019-12-04  400  40\n",
       "3  2019-12-05  500  50"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-02</th>\n",
       "      <td>200</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-04</th>\n",
       "      <td>400</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-05</th>\n",
       "      <td>500</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pv  uv\n",
       "pdate              \n",
       "2019-12-01  100  10\n",
       "2019-12-02  200  20\n",
       "2019-12-04  400  40\n",
       "2019-12-05  500  50"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new2 = df.set_index(pd.to_datetime(df[\"pdate\"])).drop(\"pdate\", axis=1)\n",
    "df_new2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-12-01', '2019-12-02', '2019-12-04', '2019-12-05'], dtype='datetime64[ns]', name='pdate', freq=None)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new2.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、使用dataframe的resample的方法按照天重采样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "resample的含义：  \n",
    "改变数据的时间频率，比如把天数据变成月份，或者把小时数据变成分钟级别\n",
    "\n",
    "resample的语法：    \n",
    "(DataFrame or Series).resample(arguments).(aggregate function)\n",
    "\n",
    "resample的采样规则参数：  \n",
    "https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-02</th>\n",
       "      <td>200.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-04</th>\n",
       "      <td>400.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-05</th>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pv    uv\n",
       "pdate                  \n",
       "2019-12-01  100.0  10.0\n",
       "2019-12-02  200.0  20.0\n",
       "2019-12-03    0.0   0.0\n",
       "2019-12-04  400.0  40.0\n",
       "2019-12-05  500.0  50.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由于采样会让区间变成一个值，所以需要指定mean等采样值的设定方法\n",
    "df_new2 = df_new2.resample(\"D\").mean().fillna(0)\n",
    "df_new2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>150.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-03</th>\n",
       "      <td>200.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-05</th>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pv    uv\n",
       "pdate                  \n",
       "2019-12-01  150.0  15.0\n",
       "2019-12-03  200.0  20.0\n",
       "2019-12-05  500.0  50.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# resample的使用方式\n",
    "df_new2.resample(\"2D\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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