{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "from LIB.BACKEND import DBManager, Log\n",
    "from pandasql import sqldf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = (datetime.datetime.now()+datetime.timedelta(days=-30)).strftime('%Y-%m-%d %H:%M:%S')\n",
    "end_time=datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "dbManager = DBManager.DBManager()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "idlist=['PJXCLL128N22C5001','PJXCLL128N22C5002','PJXCLL128N22C5003','PJXCLL128N22C5004']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bms=pd.DataFrame()\n",
    "df_gps=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sn in idlist:\n",
    "    df_data = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms','gps'])\n",
    "    df_data_bms=df_data['bms'] \n",
    "    df_data_bms[\"sn\"]=sn\n",
    "    df_data_gps=df_data['gps']\n",
    "    df_data_gps[\"sn\"]=sn\n",
    "    df_bms=pd.concat([df_bms,df_data_bms],axis=0,ignore_index=True)\n",
    "    df_gps=pd.concat([df_gps,df_data_gps],axis=0,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bms.to_csv('qiyuandata/BMS.csv',index=False)\n",
    "df_gps.to_csv('qiyuandata/GPS.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "##基于本地拉取数据，在生产上不需要\n",
    "df_bms=pd.read_csv(\"qiyuandata/BMS.csv\")\n",
    "df_gps=pd.read_csv(\"qiyuandata/GPS.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "##确定是按 sn 和 时间戳排序\n",
    "\n",
    "df_mbs=df_bms.sort_values([\"sn\",\"时间戳\"],ascending = [True, True])\n",
    "\n",
    "df_gps=df_gps.sort_values([\"sn\",\"时间戳\"],ascending = [True, True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "##寻找电压字段，计算每行的最大电压,供后续关联使用\n",
    "column=list(df_mbs.columns)\n",
    "cellvolt_list = [s for s in column if '单体电压' in s]\n",
    "df_v=df_mbs[cellvolt_list]\n",
    "df_mbs[\"v_max\"]=df_v.max(axis=1)\n",
    "df_join=df_mbs[[\"sn\",\"时间戳\",\"v_max\"]]\n",
    "p_col=['sn','data_time','v_max']\n",
    "df_join.columns=p_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "###核实数据\n",
    "df_join.to_csv('qiyuandata/df_join.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "##列变量重命名\n",
    "df_bms_1=df_bms[[\"时间戳\",\"总电流[A]\",\"总电压[V]\",\"充电状态\",\"SOC[%]\",\"sn\"]]\n",
    "df_bms_1.columns=[\"data_time\",\"aa\",\"vv\",\"charge\",\"soc\",\"sn\"]\n",
    "df_bms_2=df_bms_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "##核实数据\n",
    "df_bms_2.to_csv('qiyuandata/BMS_2.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###按充电状态第一次分组\n",
    "df_bms_2['block'] = (df_bms_2[\"charge\"].shift(1) != df_bms_2[\"charge\"]).astype(int).cumsum()\n",
    "##核实数据\n",
    "df_bms_2.to_csv(\"qiyuandata/df_bms_2_block.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "##统计分组的个数\n",
    "pysqldf = lambda q: sqldf(q, globals())\n",
    "sql=\"select count(*) as num1,df_bms_2.block from df_bms_2 group by df_bms_2.block  order by num1  \"\n",
    "df_block_num= pysqldf(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "##选取每段数量大于5的分组\n",
    "df_block_target=df_block_num[ df_block_num[\"num1\"] >5  ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "###行驶的数据\n",
    "sql1=\"select * from df_bms_2  where  charge=0 and  block in (select block from df_block_target)\"\n",
    "df_drive=pysqldf(sql1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "##核实数据\n",
    "df_drive.to_csv(\"qiyuandata/df_dirve_blcok.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "##再按时间划分\n",
    "df_drive[\"order_delta\"]= pd.to_datetime(df_drive[\"data_time\"] )-pd.to_datetime( df_drive[\"data_time\"].shift(1).fillna(method = 'backfill',axis = 0))\n",
    "df_drive[\"order_delta\"]=pd.to_timedelta(df_drive[\"order_delta\"])\n",
    "df_drive[\"order_delta\"] = df_drive[\"order_delta\"].dt.total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "##限定时间间隔1200秒切割和合并\n",
    "df_drive[\"time_flag\"]=df_drive[\"order_delta\"]>1200\n",
    "df_drive['time_block']=(df_drive[\"time_flag\"].shift(1) != df_drive[\"time_flag\"]).astype(int).cumsum()\n",
    "###核实数据\n",
    "df_drive.to_csv(\"qiyuandata/df_drive.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "##数量大于5个的分组\n",
    "sql2=\"select count(*) as num1,time_block from df_drive  group by time_block  order by num1\"\n",
    "df_drive_block_num= pysqldf(sql2)\n",
    "df_drive_time_block_target=df_drive_block_num[ df_drive_block_num[\"num1\"] >5  ] ####限定每一段的点数\n",
    "sql3=\"select * from df_drive  where   time_block in (select time_block from df_drive_time_block_target)\"\n",
    "df_drive_block=pysqldf(sql3)\n",
    "df_drive_block.to_csv(\"qiyuandata/df_drive.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "##汇总驾驶数据（包含长短驾驶）\n",
    "sql4=\"select min(data_time),max(data_time),min(soc),max(soc),time_block,sn from df_drive_block group by  sn,  time_block\"\n",
    "df_drive_static=pysqldf(sql4)\n",
    "df_drive_static.to_csv(\"onlinedata/df_drive_static.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "##选取长驾驶\n",
    "df_drive_time_block_target_l=df_drive_block_num[ df_drive_block_num[\"num1\"] >120  ] ####限定每一段的点数\n",
    "sql31=\"select * from df_drive  where   time_block in (select time_block from df_drive_time_block_target_l)\"\n",
    "df_drive_block_l=pysqldf(sql31)\n",
    "##核实数据\n",
    "df_drive_block_l.to_csv(\"qiyuandata/df_drive_l.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "###统计长驾驶\n",
    "sql41=\"select min(data_time),max(data_time),min(soc),max(soc),time_block,sn from df_drive_block_l group by  sn, time_block\"\n",
    "df_drive_static_l=pysqldf(sql41)\n",
    "##核实数据\n",
    "df_drive_static_l.to_csv(\"qiyuandata/df_drive_static_l.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "###筛选充电\n",
    "sql5=\"select * from df_bms_2  where  charge=6  and  block in (select block from df_block_target)\"\n",
    "df_charge=pysqldf(sql5)\n",
    "df_charge.to_csv(\"qiyuandata/df_charge.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "##进一步根据时间分段\n",
    "df_charge[\"order_delta\"]= pd.to_datetime(df_charge[\"data_time\"] )-pd.to_datetime( df_charge[\"data_time\"].shift(1).fillna(method = 'backfill',axis = 0))\n",
    "df_charge[\"order_delta\"]=pd.to_timedelta(df_charge[\"order_delta\"])\n",
    "df_charge[\"order_delta\"] = df_charge[\"order_delta\"].dt.total_seconds()\n",
    "df_charge[\"order_delta\"].describe()\n",
    "df_charge[\"time_flag\"]=df_charge[\"order_delta\"]>1200  ####限定时间间隔\n",
    "df_charge['time_block']=(df_charge[\"time_flag\"].shift(1) != df_charge[\"time_flag\"]).astype(int).cumsum()\n",
    "df_charge.to_csv(\"qiyuandata/df_charge.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "##排除单点充电\n",
    "sql6=\"select count(*) as num1,time_block from df_charge  group by time_block  order by num1\"\n",
    "df_charge_block_num= pysqldf(sql6)\n",
    "df_charge_block_target=df_charge_block_num[ df_charge_block_num[\"num1\"] >5  ] ####限定每一段的点数\n",
    "sql7=\"select * from df_charge  where   time_block in (select time_block from  df_charge_block_target)\"\n",
    "df_charge=pysqldf(sql7)\n",
    "df_charge.to_csv(\"qiyuandata/df_charge.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "##充电数据统计\n",
    "sql10=\"select min(data_time),max(data_time),min(soc),max(soc),time_block,sn from df_charge group by  sn,time_block\"\n",
    "df_charge_static=pysqldf(sql10)\n",
    "df_charge_static.to_csv(\"qiyuandata/df_charge_static.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_charge_clumns=[\"charge_time_b\",\"charge_time_e\",\"soc_min\",\"soc_max\",\"time_block\",\"sn\"]\n",
    "df_charge_static.columns=new_charge_clumns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_charge_static.to_csv(\"qiyuandata/df_charge_static.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "##进行指标加工，长驾驶等，没有计算短驾驶的指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1_col=[\"data_time_b\",\"data_time_e\",\"soc_min\",\"soc_max\",\"time_block\",\"sn\"]\n",
    "df_drive_static_l.columns=p1_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "##数据核实\n",
    "df_drive_static_l.to_csv(\"onlinedata/df_stand_static.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "##关联最大电压\n",
    "sql11=\"select a.*, b.v_max as v_max_b ,c.v_max as v_max_e  from df_drive_static_l a left join df_join b  on a.data_time_b=b.data_time and a.sn=b.sn  \\\n",
    "left join  df_join c on     a.data_time_e=c.data_time and a.sn=c.sn \"\n",
    "drive_statics_l_add=pysqldf(sql11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_statics_l_add.to_csv(\"qiyuandata/drive_statics_l_add.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "###计算时间差\n",
    "drive_statics_l_add[\"time_diff\"]= pd.to_datetime(drive_statics_l_add[\"data_time_e\"] )-pd.to_datetime(drive_statics_l_add[\"data_time_b\"])\n",
    "drive_statics_l_add[\"time_diff\"]=(drive_statics_l_add[\"time_diff\"].dt.total_seconds()/3600).round(2)\n",
    "sql12=\"select substring(data_time_b,1,10)  from    drive_statics_l_add \"\n",
    "drive_statics_l_add[\"day\"]=pysqldf(sql12)\n",
    "drive_statics_l_add[\"hour_b\"]=pd.to_datetime(drive_statics_l_add[\"data_time_b\"]).dt.hour\n",
    "drive_statics_l_add[\"hour_e\"]=pd.to_datetime(drive_statics_l_add[\"data_time_e\"]).dt.hour\n",
    "\n",
    "def Timephased(x):\n",
    "    if  x>=0 and x<=2 :\n",
    "        return \"D-0-2\"\n",
    "    elif x>2 and x<=4:\n",
    "        return \"D-2-4\"\n",
    "    elif x>4 and x<=6:\n",
    "        return \"D-4-6\"\n",
    "    elif x>6 and x<=8:\n",
    "        return \"D-6-8\"\n",
    "    elif x>8 and x<=10:\n",
    "        return \"D-8-10\"\n",
    "    elif x>10 and x<=12:\n",
    "        return \"D-10-12\"\n",
    "    elif x>12 and x<=14:\n",
    "        return \"D-12-14\"\n",
    "    elif x>14 and x<=16:\n",
    "        return \"D-14-16\"\n",
    "    elif x>16 and x<=18:\n",
    "        return \"D-16-18\"\n",
    "    elif x>18 and x<=20:\n",
    "        return \"D-18-20\"\n",
    "    elif x>20 and x<=22:\n",
    "        return \"D-20-22\"\n",
    "    elif x>22 and x<=24:\n",
    "        return \"D-22-24\"\n",
    "drive_statics_l_add[\"time_phase_b\"]=drive_statics_l_add[\"hour_b\"].apply(lambda x: Timephased(x))\n",
    "drive_statics_l_add[\"time_phase_e\"]=drive_statics_l_add[\"hour_e\"].apply(lambda x: Timephased(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "##计算 SOC 差值\n",
    "drive_statics_l_add[\"soc_diff\"]=drive_statics_l_add[\"soc_max\"]-drive_statics_l_add[\"soc_min\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "####计算 错行 SOC 差值 \n",
    "####第二个行程最大SOC 减去本行程最小的SOC值，来判断本行程结束是否换电\n",
    "drive_statics_l_add[\"soc_diff_u\"]  = (drive_statics_l_add[\"soc_max\"].shift(-1).fillna(method = 'ffill',axis = 0) -drive_statics_l_add[\"soc_min\"]).abs()/drive_statics_l_add[\"soc_min\"]\n",
    "def Changestatus(x):\n",
    "    if x>0.2:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "drive_statics_l_add[\"end_change\"]=drive_statics_l_add[\"soc_diff_u\"].apply(lambda x: Changestatus(x)).astype(\"int\")\n",
    "drive_statics_l_add[\"begin_change\"]  = drive_statics_l_add[\"end_change\"].shift(1).fillna(method = 'bfill',axis = 0).astype(\"int\") \n",
    "drive_statics_l_add[\"drive_block\"]=drive_statics_l_add[\"begin_change\"].cumsum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_statics_l_add.to_csv(\"qiyuandata/drive_statics_l_add.csv\",index=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "###关联充电表\n",
    "##提取行驶后充电的数据\n",
    "data_drive_charge=drive_statics_l_add[drive_statics_l_add[\"end_change\"]==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "##核实数据\n",
    "data_drive_charge.to_csv(\"qiyuandata/data_drive_charge.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "##只统计等待小于三个小时的数据，不然关联不到\n",
    "sql13=\"select a.* ,b.charge_time_b from data_drive_charge a left join df_charge_static b on a.sn=b.sn and  \\\n",
    "JULIANDAY(b.charge_time_b)-JULIANDAY(a.data_time_e)<0.125 and JULIANDAY(b.charge_time_b)-JULIANDAY(a.data_time_e)>0 \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_drive_charge_add=pysqldf(sql13)\n",
    "##核实数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "###计算等待时间\n",
    "sql14=\"select *,(JULIANDAY(charge_time_b)-JULIANDAY(data_time_e))*24*60  wait_time from data_drive_charge_add \"\n",
    "data_drive_charge_add=pysqldf(sql14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "##核实数据\n",
    "data_drive_charge_add.to_csv(\"qiyuandata/data_drive_charge_add.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "##计算等待时间超过3小时的情况\n",
    "sql141=\"select count(*) from data_drive_charge_add where  wait_time  is null \"\n",
    "data_drive_charge_three=pysqldf(sql141)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count(*)\n",
       "0        51"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_drive_charge_three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_drive_charge_time=data_drive_charge_add[\"wait_time\"].agg(['sum','max','min','median','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sum       313.516667\n",
       "max        75.716666\n",
       "min         6.750000\n",
       "median      8.083333\n",
       "mean       14.250758\n",
       "Name: wait_time, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drive_charge_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.7749999538064"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_drive_charge_add[\"wait_time\"].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "##换电站总体指标以drive_statics_l_add为主表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "sqlz1=\"select data_time_e,substring(data_time_e,1,10) day,soc_diff_u*soc_min*2.82 pow from  drive_statics_l_add where end_change=1 \"\n",
    "pow_count=pysqldf(sqlz1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>data_time_e</th>\n",
       "      <th>day</th>\n",
       "      <th>pow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-12-09 19:30:13</td>\n",
       "      <td>2022-12-09</td>\n",
       "      <td>53.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-12-09 21:35:28</td>\n",
       "      <td>2022-12-09</td>\n",
       "      <td>78.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-12-10 02:35:06</td>\n",
       "      <td>2022-12-10</td>\n",
       "      <td>217.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-12-12 16:37:40</td>\n",
       "      <td>2022-12-12</td>\n",
       "      <td>234.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-12-12 22:37:09</td>\n",
       "      <td>2022-12-12</td>\n",
       "      <td>225.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2022-12-16 17:47:44</td>\n",
       "      <td>2022-12-16</td>\n",
       "      <td>163.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2022-12-23 14:39:32</td>\n",
       "      <td>2022-12-23</td>\n",
       "      <td>222.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2022-12-24 12:23:34</td>\n",
       "      <td>2022-12-24</td>\n",
       "      <td>250.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2022-12-25 10:56:05</td>\n",
       "      <td>2022-12-25</td>\n",
       "      <td>219.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2022-12-25 17:24:21</td>\n",
       "      <td>2022-12-25</td>\n",
       "      <td>225.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            data_time_e         day     pow\n",
       "0   2022-12-09 19:30:13  2022-12-09   53.58\n",
       "1   2022-12-09 21:35:28  2022-12-09   78.96\n",
       "2   2022-12-10 02:35:06  2022-12-10  217.14\n",
       "3   2022-12-12 16:37:40  2022-12-12  234.06\n",
       "4   2022-12-12 22:37:09  2022-12-12  225.60\n",
       "..                  ...         ...     ...\n",
       "68  2022-12-16 17:47:44  2022-12-16  163.56\n",
       "69  2022-12-23 14:39:32  2022-12-23  222.78\n",
       "70  2022-12-24 12:23:34  2022-12-24  250.98\n",
       "71  2022-12-25 10:56:05  2022-12-25  219.96\n",
       "72  2022-12-25 17:24:21  2022-12-25  225.60\n",
       "\n",
       "[73 rows x 3 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pow_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "sqlz2=\"select day,sum(pow) pow_sum from pow_count  group by day\"\n",
    "pow_count_day=pysqldf(sqlz2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '换电量')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig=plt.figure(figsize=(20,5))\n",
    "plt.plot(pow_count_day[\"day\"],pow_count_day[\"pow_sum\"],ls='-',color=\"green\")\n",
    "plt.title('月换电量趋势图')  # 折线图标题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示汉字\n",
    "plt.xlabel('日期')  # x轴标题\n",
    "plt.ylabel('换电量')  # y轴标题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "pow_static=pow_count[\"pow\"].agg(['sum','max','min','median','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sum       12737.940000\n",
       "max         262.260000\n",
       "min          33.840000\n",
       "median      214.320000\n",
       "mean        174.492329\n",
       "Name: pow, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pow_static"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "##在用的电池数量\n",
    "sql15=\"select count(distinct sn)  from drive_statics_l_add \"\n",
    "count_active=pysqldf(sql15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "##换电次数\n",
    "sql16=\"select count(end_change)  from drive_statics_l_add where end_change=1 \"\n",
    "count_change=pysqldf(sql16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>count(end_change)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count(end_change)\n",
       "0                 73"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##总数\n",
    "count_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "##汇总每天的换电次数，折线图表示\n",
    "sql161=\"select count(end_change) change_num, day   from drive_statics_l_add where end_change=1   group by    day   \"\n",
    "\n",
    "count_change_day=pysqldf(sql161)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>change_num</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>2022-12-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>2022-12-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>2022-12-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>2022-12-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>2022-12-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>2022-12-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-12-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-12-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-12-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-12-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    change_num         day\n",
       "0            7  2022-12-09\n",
       "1            2  2022-12-10\n",
       "2            7  2022-12-12\n",
       "3            8  2022-12-13\n",
       "4           14  2022-12-14\n",
       "5           17  2022-12-15\n",
       "6           11  2022-12-16\n",
       "7            1  2022-12-21\n",
       "8            1  2022-12-22\n",
       "9            1  2022-12-23\n",
       "10           1  2022-12-24\n",
       "11           2  2022-12-25\n",
       "12           1  2022-12-27"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_change_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_change_day.to_csv(\"qiyuandata/count_change_day.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '换电次数')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig=plt.figure(figsize=(20,5))\n",
    "plt.plot(count_change_day[\"day\"],count_change_day[\"change_num\"],ls='-')\n",
    "plt.title('月换电次数趋势图')  # 折线图标题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示汉字\n",
    "plt.xlabel('日期')  # x轴标题\n",
    "plt.ylabel('换电次数')  # y轴标题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "count_change_day=pd.read_csv(\"qiyuandata/count_change_day.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '换电次数')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig=plt.figure(figsize=(20,5))\n",
    "plt.plot(count_change_day[\"day\"][:11],count_change_day[\"change_num\"][:11],ls='-')\n",
    "plt.plot(count_change_day[\"day\"][10:],count_change_day[\"change_num\"][10:],ls='-',color=\"red\")\n",
    "plt.title('月换电次数趋势图')  # 折线图标题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示汉字\n",
    "plt.xlabel('日期')  # x轴标题\n",
    "plt.ylabel('换电次数')  # y轴标题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "##行驶后换电时间点\n",
    "sql17=\"select count(*) change_num,time_phase_e    from drive_statics_l_add where end_change=1  group by time_phase_e \"\n",
    "change_time_slot=pysqldf(sql17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>change_num</th>\n",
       "      <th>time_phase_e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>D-0-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>D-10-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>D-12-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>D-14-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>D-16-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>D-18-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>D-2-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>D-20-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>D-22-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>D-4-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>D-6-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>D-8-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    change_num time_phase_e\n",
       "0           13        D-0-2\n",
       "1            9      D-10-12\n",
       "2            5      D-12-14\n",
       "3            9      D-14-16\n",
       "4           12      D-16-18\n",
       "5            6      D-18-20\n",
       "6            2        D-2-4\n",
       "7            4      D-20-22\n",
       "8            1      D-22-24\n",
       "9            4        D-4-6\n",
       "10           3        D-6-8\n",
       "11           5       D-8-10"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "change_time_slot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "change_time_slot.to_csv(\"qiyuandata/change_time_slot.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1ccb7303fd0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb72db6a0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb72dbd30>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb72d6400>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb72d6a90>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb34e0160>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb34e07f0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb23c7d90>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb7aa67f0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb33d0ac0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb7303fa0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb340f760>],\n",
       " [Text(0.9322950039506923, 0.5838030709139673, 'D-0-2'),\n",
       "  Text(0.0709592553256987, 1.0977088794774426, 'D-10-12'),\n",
       "  Text(-0.563608582721507, 0.9446403365740076, 'D-12-14'),\n",
       "  Text(-0.9996994132865427, 0.45891293626846325, 'D-14-16'),\n",
       "  Text(-0.9790303241085503, -0.5014973823220886, 'D-16-18'),\n",
       "  Text(-0.3489104027304354, -1.0431977429358663, 'D-18-20'),\n",
       "  Text(0.023667719874231795, -1.0997453519046831, 'D-2-4'),\n",
       "  Text(0.30370668057703176, -1.0572427593381195, 'D-20-22'),\n",
       "  Text(0.5224461870333404, -0.9680134201829663, 'D-22-24'),\n",
       "  Text(0.7170889008157263, -0.8341363847278777, 'D-4-6'),\n",
       "  Text(0.9322950176155888, -0.5838030490920284, 'D-6-8'),\n",
       "  Text(1.074632342431448, -0.23487300526092744, 'D-8-10')],\n",
       " [Text(0.7204097757800804, 0.45112055479715657, '17.81%'),\n",
       "  Text(0.05483215184258536, 0.8482295886871146, '12.33%'),\n",
       "  Text(-0.4355157230120736, 0.7299493509890058, '6.85%'),\n",
       "  Text(-0.7724950011759647, 0.35461454166199424, '12.33%'),\n",
       "  Text(-0.7565234322656978, -0.3875207045216139, '16.44%'),\n",
       "  Text(-0.2696125839280637, -0.8061073468140784, '8.22%'),\n",
       "  Text(0.018288692630088206, -0.8498032264718005, '2.74%'),\n",
       "  Text(0.23468243499134273, -0.8169603140340014, '5.48%'),\n",
       "  Text(0.40370841725303575, -0.748010370141383, '1.37%'),\n",
       "  Text(0.5541141506303339, -0.6445599336533598, '5.48%'),\n",
       "  Text(0.7204097863393185, -0.45112053793474916, '4.11%'),\n",
       "  Text(0.8303977191515732, -0.18149277679253484, '6.85%')])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot  as plt\n",
    "plt.pie(change_time_slot[\"change_num\"],labels=change_time_slot[\"time_phase_e\"],labeldistance=1.1,pctdistance=0.85,autopct='%0.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "##换电后出发的时间段分布\n",
    "sql18=\"select count(*) change_num ,time_phase_b    from drive_statics_l_add where begin_change=1  group by time_phase_b  order by time_phase_b\"\n",
    "start_time_slot=pysqldf(sql18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time_slot.to_csv(\"qiyuandata/start_time_slot.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1ccb2faea90>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb34e0d90>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb478b370>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb478ba00>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb478c0d0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb478c760>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb478cdf0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb7d6c4f0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb7d6cb80>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb7d59250>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb2faeeb0>,\n",
       "  <matplotlib.patches.Wedge at 0x1ccb7d59f40>],\n",
       " [Text(1.0635325955061894, 0.2808886225817419, 'D-0-2'),\n",
       "  Text(0.6427279972883246, 0.8926929603742485, 'D-10-12'),\n",
       "  Text(-0.21169672597739203, 1.0794371200817827, 'D-12-14'),\n",
       "  Text(-0.7523113498919248, 0.8025133225210593, 'D-14-16'),\n",
       "  Text(-1.074632336933848, 0.2348730304145128, 'D-16-18'),\n",
       "  Text(-0.9322950312804854, -0.5838030272700879, 'D-18-20'),\n",
       "  Text(-0.5224462096913297, -0.9680134079542304, 'D-2-4'),\n",
       "  Text(-0.2116967765094903, -1.0794371101715428, 'D-20-22'),\n",
       "  Text(0.023667668391436837, -1.0997453530126478, 'D-22-24'),\n",
       "  Text(0.21169670071134306, -1.079437125036902, 'D-4-6'),\n",
       "  Text(0.6037272178486238, -0.9195180511706992, 'D-6-8'),\n",
       "  Text(1.0354490328358488, -0.37127523537034524, 'D-8-10')],\n",
       " [Text(0.8218206419820554, 0.21705029926770963, '8.22%'),\n",
       "  Text(0.4966534524500689, 0.6898081966528282, '13.70%'),\n",
       "  Text(-0.16358383370980292, 0.8341105018813775, '12.33%'),\n",
       "  Text(-0.58133149764376, 0.6201239310390003, '5.48%'),\n",
       "  Text(-0.830397714903428, 0.18149279622939624, '13.70%'),\n",
       "  Text(-0.7204097968985569, -0.45112052107234063, '10.96%'),\n",
       "  Text(-0.40370843476148205, -0.7480103606919053, '5.48%'),\n",
       "  Text(-0.16358387275733338, -0.8341104942234647, '4.11%'),\n",
       "  Text(0.018288652847928466, -0.849803227327955, '2.74%'),\n",
       "  Text(0.1635838141860378, -0.8341105057103332, '2.74%'),\n",
       "  Text(0.4665164865193911, -0.7105366759046311, '9.59%'),\n",
       "  Text(0.8001197071913377, -0.2868945000589031, '10.96%')])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot  as plt\n",
    "plt.pie(start_time_slot[\"change_num\"],labels=start_time_slot[\"time_phase_b\"],labeldistance=1.1,pctdistance=0.85,autopct='%0.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "##充电情况分析主表df_charge_static\n",
    "charge_columns=['data_time_b','data_time_e','soc_min','soc_max','time_block','sn']\n",
    "df_charge_static.columns=charge_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "##计算时间差\n",
    "df_charge_static[\"time_diff\"]= pd.to_datetime(df_charge_static[\"data_time_e\"] )-pd.to_datetime(df_charge_static[\"data_time_b\"])\n",
    "df_charge_static[\"time_diff\"]=(df_charge_static[\"time_diff\"].dt.total_seconds()/3600).round(2)\n",
    "sql12=\"select substring(data_time_b,1,10)  from    df_charge_static \"\n",
    "df_charge_static[\"day\"]=pysqldf(sql12)\n",
    "df_charge_static[\"hour_b\"]=pd.to_datetime(df_charge_static[\"data_time_b\"]).dt.hour\n",
    "df_charge_static[\"hour_e\"]=pd.to_datetime(df_charge_static[\"data_time_e\"]).dt.hour\n",
    "\n",
    "def Timephased(x):\n",
    "    if  x>=0 and x<=2 :\n",
    "        return \"D-0-2\"\n",
    "    elif x>2 and x<=4:\n",
    "        return \"D-2-4\"\n",
    "    elif x>4 and x<=6:\n",
    "        return \"D-4-6\"\n",
    "    elif x>6 and x<=8:\n",
    "        return \"D-6-8\"\n",
    "    elif x>8 and x<=10:\n",
    "        return \"D-8-10\"\n",
    "    elif x>10 and x<=12:\n",
    "        return \"D-10-12\"\n",
    "    elif x>12 and x<=14:\n",
    "        return \"D-12-14\"\n",
    "    elif x>14 and x<=16:\n",
    "        return \"D-14-16\"\n",
    "    elif x>16 and x<=18:\n",
    "        return \"D-16-18\"\n",
    "    elif x>18 and x<=20:\n",
    "        return \"D-18-20\"\n",
    "    elif x>20 and x<=22:\n",
    "        return \"D-20-22\"\n",
    "    elif x>22 and x<=24:\n",
    "        return \"D-22-24\"\n",
    "\n",
    "df_charge_static[\"time_phase_b\"]=df_charge_static[\"hour_b\"].apply(lambda x: Timephased(x))\n",
    "df_charge_static[\"time_phase_e\"]=df_charge_static[\"hour_e\"].apply(lambda x: Timephased(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "##计算 SOC 差值\n",
    "df_charge_static[\"soc_diff\"]=df_charge_static[\"soc_max\"]-df_charge_static[\"soc_min\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "##选取充电时间大于12分钟的时间段\n",
    "df_charge_static=df_charge_static[df_charge_static[\"time_diff\"]>0.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "##充电时间统计\n",
    "df_charge_static_time=df_charge_static[\"time_diff\"].agg(['sum','max','min','median','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_charge_static_time_75=df_charge_static[\"time_diff\"].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sum       21.820000\n",
       "max        1.100000\n",
       "min        0.440000\n",
       "median     0.870000\n",
       "mean       0.808148\n",
       "Name: time_diff, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_charge_static_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_charge_static_time_75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "##充电SOC变化统计\n",
    "df_charge_static_soc=df_charge_static[\"soc_diff\"].agg(['sum','max','min','median','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sum       1674.0\n",
       "max         82.0\n",
       "min         26.0\n",
       "median      70.0\n",
       "mean        62.0\n",
       "Name: soc_diff, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_charge_static_soc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_charge_static_soc_75=df_charge_static[\"soc_diff\"].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.5"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_charge_static_soc_75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "##充电时间段的情况\n",
    "df_charge_static_phase=df_charge_static[[\"time_phase_b\",\"time_block\"]].groupby(\"time_phase_b\").agg(['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "##充电结束SOC分布情况\n",
    "df_charge_static_soc_e=df_charge_static[\"soc_max\"].agg(['max','min','median','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max       100.000000\n",
       "min        98.000000\n",
       "median     98.000000\n",
       "mean       98.074074\n",
       "Name: soc_max, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_charge_static_soc_e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_charge_static_soc_e75=df_charge_static[\"soc_max\"].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_charge_static_soc_e75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "##充电前SOC分布情况\n",
    "df_charge_static_soc_b=df_charge_static[\"soc_min\"].agg(['max','min','median','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max       72.000000\n",
       "min       16.000000\n",
       "median    28.000000\n",
       "mean      36.074074\n",
       "Name: soc_min, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_charge_static_soc_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_charge_static_soc_b75=df_charge_static[\"soc_min\"].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_charge_static_soc_b75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "##针对电池进行分析,主表drive_statics_l_add为主表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "##本月支撑行驶的次数\n",
    "drive_count_total=drive_statics_l_add[[\"sn\",\"drive_block\"]].groupby(\"sn\").agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_count_total.columns=[\"count_total\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>count_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sn</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5001</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5002</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5003</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5004</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count_total\n",
       "sn                            \n",
       "PJXCLL128N22C5001           28\n",
       "PJXCLL128N22C5002           12\n",
       "PJXCLL128N22C5003           19\n",
       "PJXCLL128N22C5004           27"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_count_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_count_total.to_csv(\"qiyuandata/drive_count_total.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "##本月支撑行驶的次数\n",
    "drive_count_total_day=drive_statics_l_add[[\"sn\",\"day\",\"drive_block\"]].groupby([\"sn\",\"day\"]).agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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></th>\n",
       "      <th>drive_block</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sn</th>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">PJXCLL128N22C5001</th>\n",
       "      <th>2022-12-09</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-12</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-13</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-15</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-16</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-21</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-22</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">PJXCLL128N22C5002</th>\n",
       "      <th>2022-12-09</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-15</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-16</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">PJXCLL128N22C5003</th>\n",
       "      <th>2022-12-09</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-12</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-13</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-15</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-16</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-25</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">PJXCLL128N22C5004</th>\n",
       "      <th>2022-12-08</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-09</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-12</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-13</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-14</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-15</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-16</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-23</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-24</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-25</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              drive_block\n",
       "sn                day                    \n",
       "PJXCLL128N22C5001 2022-12-09            3\n",
       "                  2022-12-10            1\n",
       "                  2022-12-12            2\n",
       "                  2022-12-13            3\n",
       "                  2022-12-14            3\n",
       "                  2022-12-15            8\n",
       "                  2022-12-16            4\n",
       "                  2022-12-21            3\n",
       "                  2022-12-22            1\n",
       "PJXCLL128N22C5002 2022-12-09            2\n",
       "                  2022-12-14            3\n",
       "                  2022-12-15            4\n",
       "                  2022-12-16            3\n",
       "PJXCLL128N22C5003 2022-12-09            2\n",
       "                  2022-12-10            1\n",
       "                  2022-12-12            2\n",
       "                  2022-12-13            2\n",
       "                  2022-12-14            5\n",
       "                  2022-12-15            4\n",
       "                  2022-12-16            2\n",
       "                  2022-12-25            1\n",
       "PJXCLL128N22C5004 2022-12-08            1\n",
       "                  2022-12-09            2\n",
       "                  2022-12-10            1\n",
       "                  2022-12-12            3\n",
       "                  2022-12-13            3\n",
       "                  2022-12-14            5\n",
       "                  2022-12-15            3\n",
       "                  2022-12-16            3\n",
       "                  2022-12-23            2\n",
       "                  2022-12-24            1\n",
       "                  2022-12-25            3"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_count_total_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_count_total_day_qq=drive_count_total_day.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_count_total_day_qq.to_csv(\"qiyuandata/drive_count_total_day_qq.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "qq1=drive_count_total_day_qq[drive_count_total_day_qq[\"sn\"]==\"PJXCLL128N22C5001\"]\n",
    "qq2=drive_count_total_day_qq[drive_count_total_day_qq[\"sn\"]==\"PJXCLL128N22C5002\"]\n",
    "qq3=drive_count_total_day_qq[drive_count_total_day_qq[\"sn\"]==\"PJXCLL128N22C5003\"]\n",
    "qq4=drive_count_total_day_qq[drive_count_total_day_qq[\"sn\"]==\"PJXCLL128N22C5004\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2673d01c490>]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig=plt.figure(figsize=(20,5))\n",
    "plt.plot(qq1[\"day\"],qq1[\"drive_block\"],ls='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2673fc570a0>]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig=plt.figure(figsize=(20,5))\n",
    "plt.plot(qq2[\"day\"],qq2[\"drive_block\"],ls='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2673cae20d0>]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig=plt.figure(figsize=(20,5))\n",
    "plt.plot(qq3[\"day\"],qq3[\"drive_block\"],ls='-')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2673b4154f0>]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig=plt.figure(figsize=(20,5))\n",
    "plt.plot(qq4[\"day\"],qq4[\"drive_block\"],ls='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##统一到同一个坐标系中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "##单次行驶的时间\n",
    "drive_time_single=drive_statics_l_add[[\"sn\",\"time_diff\"]].groupby(\"sn\").agg(['sum','max','min','median','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">time_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5001</th>\n",
       "      <td>75.89</td>\n",
       "      <td>6.77</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.955</td>\n",
       "      <td>2.710357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5002</th>\n",
       "      <td>47.94</td>\n",
       "      <td>6.44</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.335</td>\n",
       "      <td>3.995000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5003</th>\n",
       "      <td>83.38</td>\n",
       "      <td>7.36</td>\n",
       "      <td>0.21</td>\n",
       "      <td>4.850</td>\n",
       "      <td>4.388421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5004</th>\n",
       "      <td>125.12</td>\n",
       "      <td>22.09</td>\n",
       "      <td>1.36</td>\n",
       "      <td>4.330</td>\n",
       "      <td>4.634074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time_diff                              \n",
       "                        sum    max   min median      mean\n",
       "sn                                                       \n",
       "PJXCLL128N22C5001     75.89   6.77  0.39  1.955  2.710357\n",
       "PJXCLL128N22C5002     47.94   6.44  0.04  4.335  3.995000\n",
       "PJXCLL128N22C5003     83.38   7.36  0.21  4.850  4.388421\n",
       "PJXCLL128N22C5004    125.12  22.09  1.36  4.330  4.634074"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_time_single"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_time_single.to_csv(\"qiyuandata/drive_time_single.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_time_single_75=drive_statics_l_add.groupby(\"sn\").time_diff.quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sn\n",
       "PJXCLL128N22C5001    4.3525\n",
       "PJXCLL128N22C5002    5.0375\n",
       "PJXCLL128N22C5003    5.8500\n",
       "PJXCLL128N22C5004    4.8400\n",
       "Name: time_diff, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_time_single_75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "##按天汇总行驶时长\n",
    "drive_time_single_day=drive_statics_l_add[[\"sn\",\"day\",\"time_diff\"]].groupby([\"sn\",\"day\"]).agg(['sum']).sort_values(by=['sn','day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_time_single_day_qq=drive_time_single_day.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_time_single_day_qq.columns=[\"sn\",\"day\",\"drive_time\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>sn</th>\n",
       "      <th>day</th>\n",
       "      <th>drive_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-09</td>\n",
       "      <td>6.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-10</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-12</td>\n",
       "      <td>9.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-13</td>\n",
       "      <td>11.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-14</td>\n",
       "      <td>12.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-15</td>\n",
       "      <td>17.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-16</td>\n",
       "      <td>9.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-21</td>\n",
       "      <td>5.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-22</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PJXCLL128N22C5002</td>\n",
       "      <td>2022-12-09</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PJXCLL128N22C5002</td>\n",
       "      <td>2022-12-14</td>\n",
       "      <td>15.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PJXCLL128N22C5002</td>\n",
       "      <td>2022-12-15</td>\n",
       "      <td>18.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PJXCLL128N22C5002</td>\n",
       "      <td>2022-12-16</td>\n",
       "      <td>10.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PJXCLL128N22C5003</td>\n",
       "      <td>2022-12-09</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PJXCLL128N22C5003</td>\n",
       "      <td>2022-12-10</td>\n",
       "      <td>4.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PJXCLL128N22C5003</td>\n",
       "      <td>2022-12-12</td>\n",
       "      <td>12.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PJXCLL128N22C5003</td>\n",
       "      <td>2022-12-13</td>\n",
       "      <td>12.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PJXCLL128N22C5003</td>\n",
       "      <td>2022-12-14</td>\n",
       "      <td>15.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PJXCLL128N22C5003</td>\n",
       "      <td>2022-12-15</td>\n",
       "      <td>19.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PJXCLL128N22C5003</td>\n",
       "      <td>2022-12-16</td>\n",
       "      <td>7.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PJXCLL128N22C5003</td>\n",
       "      <td>2022-12-25</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-08</td>\n",
       "      <td>22.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-09</td>\n",
       "      <td>8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-10</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-12</td>\n",
       "      <td>12.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-13</td>\n",
       "      <td>12.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-14</td>\n",
       "      <td>17.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-15</td>\n",
       "      <td>15.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-16</td>\n",
       "      <td>13.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-23</td>\n",
       "      <td>5.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-24</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-25</td>\n",
       "      <td>9.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sn         day  drive_time\n",
       "0   PJXCLL128N22C5001  2022-12-09        6.70\n",
       "1   PJXCLL128N22C5001  2022-12-10        0.39\n",
       "2   PJXCLL128N22C5001  2022-12-12        9.46\n",
       "3   PJXCLL128N22C5001  2022-12-13       11.15\n",
       "4   PJXCLL128N22C5001  2022-12-14       12.20\n",
       "5   PJXCLL128N22C5001  2022-12-15       17.10\n",
       "6   PJXCLL128N22C5001  2022-12-16        9.38\n",
       "7   PJXCLL128N22C5001  2022-12-21        5.28\n",
       "8   PJXCLL128N22C5001  2022-12-22        4.23\n",
       "9   PJXCLL128N22C5002  2022-12-09        4.23\n",
       "10  PJXCLL128N22C5002  2022-12-14       15.25\n",
       "11  PJXCLL128N22C5002  2022-12-15       18.18\n",
       "12  PJXCLL128N22C5002  2022-12-16       10.28\n",
       "13  PJXCLL128N22C5003  2022-12-09        6.11\n",
       "14  PJXCLL128N22C5003  2022-12-10        4.34\n",
       "15  PJXCLL128N22C5003  2022-12-12       12.21\n",
       "16  PJXCLL128N22C5003  2022-12-13       12.62\n",
       "17  PJXCLL128N22C5003  2022-12-14       15.80\n",
       "18  PJXCLL128N22C5003  2022-12-15       19.04\n",
       "19  PJXCLL128N22C5003  2022-12-16        7.15\n",
       "20  PJXCLL128N22C5003  2022-12-25        6.11\n",
       "21  PJXCLL128N22C5004  2022-12-08       22.09\n",
       "22  PJXCLL128N22C5004  2022-12-09        8.11\n",
       "23  PJXCLL128N22C5004  2022-12-10        4.33\n",
       "24  PJXCLL128N22C5004  2022-12-12       12.77\n",
       "25  PJXCLL128N22C5004  2022-12-13       12.31\n",
       "26  PJXCLL128N22C5004  2022-12-14       17.72\n",
       "27  PJXCLL128N22C5004  2022-12-15       15.22\n",
       "28  PJXCLL128N22C5004  2022-12-16       13.58\n",
       "29  PJXCLL128N22C5004  2022-12-23        5.79\n",
       "30  PJXCLL128N22C5004  2022-12-24        3.50\n",
       "31  PJXCLL128N22C5004  2022-12-25        9.70"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_time_single_day_qq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "##同一坐标系\n",
    "for i in ['PJXCLL128N22C5001','PJXCLL128N22C5002','PJXCLL128N22C5003','PJXCLL128N22C5004']:\n",
    "    for j in np.arange(9,26):\n",
    "        if len(str(j))==1:\n",
    "            b=\"0\"+str(j)\n",
    "        else:\n",
    "            b=str(j)\n",
    "        a.append([i,\"2022-12-\"+b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "##构建主表\n",
    "aa=pd.DataFrame(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa.columns=[\"sn\",\"day\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "##构建绘图数据\n",
    "sqly1=\"select a.*,b.drive_time from aa a left join drive_time_single_day_qq b on a.sn=b.sn and a.day=b.day \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa_join=pysqldf(sqly1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>sn</th>\n",
       "      <th>day</th>\n",
       "      <th>drive_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-09</td>\n",
       "      <td>6.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-10</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-12</td>\n",
       "      <td>9.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PJXCLL128N22C5001</td>\n",
       "      <td>2022-12-13</td>\n",
       "      <td>11.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-22</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-23</td>\n",
       "      <td>5.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-24</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>PJXCLL128N22C5004</td>\n",
       "      <td>2022-12-25</td>\n",
       "      <td>9.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sn         day  drive_time\n",
       "0   PJXCLL128N22C5001  2022-12-09        6.70\n",
       "1   PJXCLL128N22C5001  2022-12-10        0.39\n",
       "2   PJXCLL128N22C5001  2022-12-11         NaN\n",
       "3   PJXCLL128N22C5001  2022-12-12        9.46\n",
       "4   PJXCLL128N22C5001  2022-12-13       11.15\n",
       "..                ...         ...         ...\n",
       "63  PJXCLL128N22C5004  2022-12-21         NaN\n",
       "64  PJXCLL128N22C5004  2022-12-22         NaN\n",
       "65  PJXCLL128N22C5004  2022-12-23        5.79\n",
       "66  PJXCLL128N22C5004  2022-12-24        3.50\n",
       "67  PJXCLL128N22C5004  2022-12-25        9.70\n",
       "\n",
       "[68 rows x 3 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa_join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa_join.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa_join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "qq1=aa_join[aa_join['sn']=='PJXCLL128N22C5001']\n",
    "qq2=aa_join[aa_join['sn']=='PJXCLL128N22C5002']\n",
    "qq3=aa_join[aa_join['sn']=='PJXCLL128N22C5003']\n",
    "qq4=aa_join[aa_join['sn']=='PJXCLL128N22C5004']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig=plt.figure(figsize=(20,5))\n",
    "plt.title('支撑行驶时间')  # 折线图标题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示汉字\n",
    "plt.xlabel('日期')  # x轴标题\n",
    "plt.ylabel('行驶时间（单位小时）')  # y轴标题\n",
    "plt.plot(qq1[\"day\"],qq1[\"drive_time\"], marker='o', markersize=3)  # 绘制折线图，添加数据点，设置点的大小\n",
    "plt.plot(qq2[\"day\"],qq2[\"drive_time\"], marker='o', markersize=3)\n",
    "plt.plot(qq3[\"day\"],qq3[\"drive_time\"], marker='o', markersize=3)\n",
    "plt.plot(qq4[\"day\"],qq4[\"drive_time\"], marker='o', markersize=3)\n",
    " \n",
    "plt.legend(['PJXCLL128N22C5001', 'PJXCLL128N22C5002', 'PJXCLL128N22C5003', 'PJXCLL128N22C5004'])  # 设置折线名称\n",
    " \n",
    "plt.show()  # 显示折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "##单次行驶SOC消耗情况\n",
    "drive_soc_single=drive_statics_l_add[[\"sn\",\"soc_diff\"]].groupby(\"sn\").agg(['max','min','median','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">soc_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5001</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>46.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5002</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.5</td>\n",
       "      <td>51.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5003</th>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>59.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJXCLL128N22C5004</th>\n",
       "      <td>86.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>55.555556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  soc_diff                       \n",
       "                       max  min median       mean\n",
       "sn                                               \n",
       "PJXCLL128N22C5001     84.0  0.0   39.5  46.142857\n",
       "PJXCLL128N22C5002     81.0  0.0   53.5  51.916667\n",
       "PJXCLL128N22C5003     83.0  0.0   64.0  59.473684\n",
       "PJXCLL128N22C5004     86.0  2.0   56.0  55.555556"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_soc_single"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_soc_single.to_csv(\"qiyuandata/drive_soc_single.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_soc_single_75=drive_statics_l_add.groupby(\"sn\").soc_diff.quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sn\n",
       "PJXCLL128N22C5001    77.50\n",
       "PJXCLL128N22C5002    74.75\n",
       "PJXCLL128N22C5003    79.50\n",
       "PJXCLL128N22C5004    78.50\n",
       "Name: soc_diff, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_soc_single_75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174.84"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.62*282"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1270.1999999999998"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "73*17.4\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py38",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "4d6c15edccb966d2bb52e4527dc0611a26206d084fadc4df5e610d719652857f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}