{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好的，监控采集的光伏发电数据，晚上数据都是0，白天根据天气情况。想要获得几个操作：   \n",
    "   1.每天白天的平均值，也就是每天从第一个非零数据到最后一个非零数据之间的平均值；   \n",
    "   2.每天第一个和最后一个非零数据的时间，也就是获得第一个和最后一个非零数据所在行，发电开始和结束时间；   \n",
    "   3.每天第一个和最后一个非零数据时间差，也就是发电时长\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据   \n",
    "设置索引   \n",
    "去除时间列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('2021(1).xlsx')\n",
    "\n",
    "df.index=df['dt']\n",
    "\n",
    "df=df.drop('dt',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对应的是第一个问题，第二个问题，第三个问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def first(x):\n",
    "    return x[x[x!=0].first_valid_index():x[x!=0].last_valid_index()].mean()\n",
    "def seconed(x):\n",
    "    return x[x!=0].first_valid_index(),x[x!=0].last_valid_index()\n",
    "def third(x):\n",
    "    return x[x!=0].last_valid_index()-x[x!=0].first_valid_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df.groupby(pd.Grouper(freq='D')).apply(first)\n",
    "\n",
    "df2=df.groupby(pd.Grouper(freq='D')).apply(seconed)\n",
    "\n",
    "df3=df.groupby(pd.Grouper(freq='D')).apply(third)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "拼接操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_l=pd.concat([df1,df2,df3],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>da</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>1.2</td>\n",
       "      <td>(2021-09-06 15:07:46, 2021-09-06 15:11:46)</td>\n",
       "      <td>0 days 00:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(2021-09-07 15:07:46, 2021-09-07 15:11:46)</td>\n",
       "      <td>0 days 00:04:00</td>\n",
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      "text/plain": [
       "             da                                           0               1\n",
       "dt                                                                         \n",
       "2021-09-06  1.2  (2021-09-06 15:07:46, 2021-09-06 15:11:46) 0 days 00:04:00\n",
       "2021-09-07  1.0  (2021-09-07 15:07:46, 2021-09-07 15:11:46) 0 days 00:04:00"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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