Pro_Data_Deep2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import  pandas as pd\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "table5= pd.read_csv(r'taxi-price.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#计算订单用时\n",
    "table5['interval'] = (pd.to_datetime(table5['Etime']) -pd.to_datetime(table5['time'])).apply(lambda r:r.seconds)\n",
    "table5['speed'] = (table5['distance']/table5['interval'])*3.6\n",
    "table5 = table5[table5['speed']<80].copy()\n",
    "table5['price_per_minutes'] = table5['price']/(table5['interval']/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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       "       orderid  taxi_id                 time        lat         lon  isd  \\\n",
       "0          1.0    22224  2021-05-18 00:06:04  22.548750  114.031998    0   \n",
       "1          2.0    22224  2021-05-18 00:20:35  22.560617  114.019218    0   \n",
       "2          3.0    22224  2021-05-18 00:41:32  22.581484  114.121002    0   \n",
       "3          4.0    22224  2021-05-18 01:08:18  22.566116  114.108002    0   \n",
       "4          5.0    22224  2021-05-18 01:35:11  22.559816  114.028580    0   \n",
       "...        ...      ...                  ...        ...         ...  ...   \n",
       "77681  96265.0    36947  2021-05-18 20:40:31  22.562599  114.060631    0   \n",
       "77682  96266.0    36947  2021-05-18 21:01:39  22.539383  113.986748    0   \n",
       "77683  96267.0    36947  2021-05-18 21:17:39  22.545532  113.962067    0   \n",
       "77684  96269.0    36947  2021-05-18 22:06:32  22.531250  113.912468    0   \n",
       "77685  96271.0    36947  2021-05-18 22:46:53  22.537350  113.995453    0   \n",
       "\n",
       "                     Etime        ELng       ELat      distance  interval  \\\n",
       "0      2021-05-18 00:20:19  114.020081  22.560667   3866.158570       855   \n",
       "1      2021-05-18 00:39:41  114.121536  22.581150  13666.665576      1146   \n",
       "2      2021-05-18 01:08:03  114.108047  22.566133   6255.412700      1591   \n",
       "3      2021-05-18 01:34:55  114.028633  22.559933  13523.532187      1597   \n",
       "4      2021-05-18 06:59:56  113.887032  22.547533  38487.652998     19485   \n",
       "...                    ...         ...        ...           ...       ...   \n",
       "77681  2021-05-18 21:01:19  113.986786  22.540216   9112.138458      1248   \n",
       "77682  2021-05-18 21:17:29  113.962036  22.545750   3707.102340       950   \n",
       "77683  2021-05-18 21:54:08  113.920586  22.530268   8805.960210      2189   \n",
       "77684  2021-05-18 22:36:53  113.997284  22.545650  11354.732854      1821   \n",
       "77685  2021-05-18 23:13:15  113.922485  22.496550  13131.147097      1582   \n",
       "\n",
       "       isnight  起步价        里程价      返空费      夜间附加费       候时费       price  \\\n",
       "0         True   16   4.478781 -0.00000   0.895756  0.280000   21.654537   \n",
       "1         True   16  27.999997 -0.00000   5.599999  0.200000   49.799997   \n",
       "2         True   16  10.212990 -0.00000   2.042598  0.360000   28.615589   \n",
       "3         True   16  27.656477 -0.00000   5.531295  0.400000   49.587773   \n",
       "4         True   16  87.570367  9.71111  17.514073  3.586667  134.382217   \n",
       "...        ...  ...        ...      ...        ...       ...         ...   \n",
       "77681    False   11  17.069132 -0.00000   0.000000  0.733333   28.802466   \n",
       "77682    False   11   4.097046 -0.00000   0.000000  1.466667   16.563712   \n",
       "77683    False   11  16.334305 -0.00000   0.000000  2.933333   30.267638   \n",
       "77684    False   11  22.451359 -0.00000   0.000000  2.933333   36.384692   \n",
       "77685    False   11  26.714753 -0.00000   0.000000  0.733333   38.448086   \n",
       "\n",
       "           speed  price_per_minutes  \n",
       "0      16.278562           1.519617  \n",
       "1      42.931934           2.607330  \n",
       "2      14.154296           1.079155  \n",
       "3      30.485107           1.863035  \n",
       "4       7.110883           0.413802  \n",
       "...          ...                ...  \n",
       "77681  26.285015           1.384734  \n",
       "77682  14.047967           1.046129  \n",
       "77683  14.482164           0.829629  \n",
       "77684  22.447577           1.198837  \n",
       "77685  29.881245           1.458208  \n",
       "\n",
       "[77439 rows x 20 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "table5.drop(index=table5[(table5.loc[:,'price']>400)].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = pd.DataFrame(table5.groupby('taxi_id',as_index=False)['price'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      taxi_id        price\n",
       "0       22224  1238.156861\n",
       "1       22228  1493.497395\n",
       "2       22231   961.278354\n",
       "3       22236   904.188903\n",
       "4       22241  1138.755000\n",
       "...       ...          ...\n",
       "2313    36878   669.417640\n",
       "2314    36886  1222.063839\n",
       "2315    36899  1530.850336\n",
       "2316    36920  1467.869021\n",
       "2317    36947   890.462032\n",
       "\n",
       "[2318 rows x 2 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
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       "      taxi_id       distance        cost\n",
       "0       22224  371309.178404  278.481884\n",
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       "2315    36899  425178.871084  318.884153\n",
       "2316    36920  370814.369809  278.110777\n",
       "2317    36947  280090.350286  210.067763\n",
       "\n",
       "[2318 rows x 3 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1= pd.DataFrame(table5.groupby('taxi_id',as_index=False)['distance'].sum())\n",
    "df1['cost'] = (df1['distance']/1000)*0.75\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义计算路径长度函数\n",
    "from math import pi\n",
    "import numpy as np\n",
    "\n",
    "def getdistance(lon1, lat1, lon2, lat2):\n",
    "    \"\"\"\n",
    "    Calculate the great circle distance between two points \n",
    "    on the earth (specified in decimal degrees)\n",
    "    \"\"\"\n",
    "    \n",
    "    lon1, lat1, lon2, lat2 = map(lambda r:r*pi/180, [lon1, lat1, lon2, lat2])\n",
    "    dlon = lon2 - lon1 \n",
    "    dlat = lat2 - lat1 \n",
    "    a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\n",
    "    c = 2 * np.arcsin(a**0.5) \n",
    "    r = 6371 \n",
    "    return c * r * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0['cost'] = (df1['distance']/1000)*0.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      taxi_id        price        cost\n",
       "0       22224  1238.156861  278.481884\n",
       "1       22228  1493.497395  297.168296\n",
       "2       22231   961.278354  211.465654\n",
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       "2316    36920  1467.869021  278.110777\n",
       "2317    36947   890.462032  210.067763\n",
       "\n",
       "[2318 rows x 3 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>2318 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "      taxi_id        price        cost       income\n",
       "0       22224  1238.156861  278.481884   959.674977\n",
       "1       22228  1493.497395  297.168296  1196.329099\n",
       "2       22231   961.278354  211.465654   749.812700\n",
       "3       22236   904.188903  200.236496   703.952407\n",
       "4       22241  1138.755000  275.602449   863.152551\n",
       "...       ...          ...         ...          ...\n",
       "2313    36878   669.417640  166.513846   502.903794\n",
       "2314    36886  1222.063839  262.609327   959.454512\n",
       "2315    36899  1530.850336  318.884153  1211.966183\n",
       "2316    36920  1467.869021  278.110777  1189.758244\n",
       "2317    36947   890.462032  210.067763   680.394270\n",
       "\n",
       "[2318 rows x 4 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0['income']=df0['price']-df0['cost']\n",
    "df0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of income')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b9eumNfKLgX1I+7jLyt82VKetbKg+84ALaoYnAw/Onj2badOmDSNq96hUKixevJi+vj56eno6HadpOed39rE3o38hvROCs/bfyBlLJzC4UZ2O1LRW89/VP2cUUw1Prn83kGf2tWsb1u5NdEUhl3QR8Cbg1RHxYM2oVcXzLsDKmvadSfvJq322ljQ1Iv5Y6nNTvflFxCAwWDN/AHp6erL5BZflnB3yzu/sY2dwwzOFb3CjNhnOTbP5u+n3lNvfTa2csg83Z6dPP5Oki4G3Aq+NiGWlLstIhbqv5jVbk452rxbp24BKqc+uwAwaFHIzM7MtRafXyC8hHX3+ZmCdpF2K9jUR8WREhKQLgdMl3QfcB5wODADzASJijaQvAedLeox0oNx5wJ3A9WO6NGZmZmOs04X8xOJ5San9eODy4udzgW2AzwFTgVuBwyNiXU3/DwBPA1cWfb8PHBcRG0YltZmZWZfo9Hnkm91BFOmw+v7i0ajPeuB9xcPMzGzc6PSV3czMzGwEOr1p3cy2UNNPvbbTEczGBRdyM3PRNcuYC7mZWQZG48vW8nOOaPs0bex5H7mZmVnGXMjNzMwy5kJuZmaWMRdyMzOzjLmQm5mZZcyF3MzMLGMu5GZmZhlzITczM8uYC7mZmVnGXMjNzMwy5kJuZmaWMRdyMzOzjLmQm5mZZcyF3MzMLGMu5GZmZhlzITczM8uYC7mZmVnGtup0ADNrzvRTrx3xNHonBuceCDP6FzK4QW1IZWad4jVyMzOzjLmQm5mZZcyF3MzMLGMu5GZmZhlzITczM8uYC7mZmVnGXMjNzMwy5kJuZmaWsZYKuaTfSZpWp30HSb8beSwzMzMbjlbXyKcDE+u09wLPG+5EJL1a0jWSVkgKSW8pjb+8aK993FLq0yvpIkmPSnpC0nck7d7CMpmZmWWnqUu0SnpTzeAcSWtqhicChwLLm5jktsAvgMuAbzbo8z3g+Jrhp0rjLwTeCBwNPAacDyyQNDMiNjSRxczMLDvNXmv96uI5gC+XxlVIRfxfhzuxiLgOuA5Aani958GIWFVvhKQpwDuAYyPi+qLtGOAB4DBg4XCzmI2GdlwX3cxsKE0V8oiYACBpGXBARDw6Kqk2dYikR4DHgRuAD0fEI8W4mUAPsKgm4wpJdwGzaFDIJfWSdgNUTQaoVCpUKpW2L8BoqubNLXdVzvmHk713YoxVnKb0TohNnnOSc3borvzNfu629M9rtxluVkV0/o8JQFIAR0bE1TVtRwF/Au4H9gLOIn35mBkRg5LmApdFRG9pWouAZRFxQoN59QNnltvnz5/PpEmT2rNAZmZmIzAwMMDcuXMBpkTE2kb9Wr6NqaRDSfvEd6Z00FxE/FOr0y1N5xs1g3dJWkoq6kcAVw0Vj7T5v5F5wAU1w5OBB2fPns20ac86GL+rVSoVFi9eTF9fHz09PZ2O07Sc8w8n+4z+7ty70zshOGv/jZyxdAKDG/O6jWnO2aG78t/VP6ep/lv657XbrF3bsHZvoqVCLulM4KPAUmAlQxfNtomIlZLuB15YNK0CtpY0NSL+WNN1Z+CmIaYzCAxWh6v753t6erL5BZflnB3yzj9U9m6/1/fgRnV9xkZyzg7dkb/Vz9yW+nntNsPN2eoa+buB4yLiihZf35Li3PU9SF8eAG4jHWTXB1xZ9NkVmAGcPJbZzMzMOqHVQr41Q6zxDpek7YAX1DTtJWk/YHXx6CedlraSdO76J4FHgW8BRMQaSV8Czpf0WPGa84A7getHms/MzKzbtXpBmP8A5rZh/vsDtxcPSPutbwc+DmwAXgZ8G/gN6XS33wAHR8S6mml8gHRa3JXAjcAA8EafQ25mZuNBq2vkzwHeJekw4Jekzdt/FhEfHM5EImIJ6cC0RjZ7JEZErAfeVzzMzMzGlVYL+T7AHcXPM0rjuuN8NjMzs3GgpUIeEbPbHcTMzMya59uYmpmZZazV88h/yBCb0CPitS0nMjMzs2FrdR/5HaXhHmA/0v7y8s1UzMzMbJS0uo/8A/Xai2uYbzeSQGZmZjZ87d5H/lWgLddZNzMzs81rdyE/GFjf5mmamZlZA60e7Fa+85iAXUlXajtrpKHMzMxseFo92G1NaXgjcC/w0YhYNLJIZmZmNlytHux2fLuDmJmZWfNaXSMHQNJM4CWkc8p/FRG3b+YlZmZm1kat7iPfGfg6cAjwOGkf+ZTiQjFHR8Qf2hXQzMzMGmv1qPWLgO2Bl0bEjhExlXQxmO2Bz7YrnJmZmQ2t1U3rrwMOi4h7qg0R8StJ/wL4YDczM7Mx0uoa+QRK9yAvVEYwTTMzM2tSq0X3B8C/Sdqt2iDpecBngO+3I5iZmZltXquF/L3AZGC5pN9K+h9gWdH2vnaFMzMzs6G1eh75A8ArJPUBLyYdtf6riLi+neHMzMxsaE0VckmvBS4GDoqItRGxGFhcjJsi6W7g3RHx4/ZHNRtd00+9tqn+vRODcw+EGf0LGdygUUplZja0ZjetnwT8e0SsLY+IiDXApcAH25DLzMzMhqHZQr4v8L0hxi8CZrYex8zMzJrRbCF/LvVPO6t6Gtip9ThmZmbWjGYL+UPAy4YYvw+wsvU4ZmZm1oxmC/l3gY9Lek55hKRtgI8BC9oRzMzMzDav2dPPPgG8FfiNpItJ9yAP0h3Q/gWYCJzd1oRmZmbWUFOFPCIeljQL+Dwwj3T+OKRivhB4T0Q83N6IZmZm1kjTF4SJiPuB10uaCryAVMzvi4g/tjucmZmZDa3Vu59RFO6ftTGLmZmZNcl3KjMzM8uYC7mZmVnGOlrIJb1a0jWSVkgKSW8pjZek/mL8k5KWSHppqU+vpIskPSrpCUnfkbT7mC6ImZlZh3R6jXxb4Bek26LWczLp2u3vBQ4AVgGLJU2u6XMhcCRwNPBKYDtggaSJo5TZzMysa7R8sFs7RMR1wHUA0qZ3j1JqOAk4OyKuKtreDjwMzAUulTQFeAdwbPUWqpKOAR4ADiOdEmdmZrbF6mgh34y9gF1IN2IBICIGJd0AzCLdaW0m0FPqs0LSXUWfuoVcUi/QW9M0GaBSqVCpDHUp+e5TzZtb7qpuyt87MZrrPyE2ec6Js3dON+Vv9nPXTZ/XZuWYfbhZFdH5PyYASQEcGRFXF8OzgBuB50XEipp+XwT2jIg5kuYCl0VEb2lai4BlEXFCg3n1A2eW2+fPn8+kSZPatERmZmatGxgYYO7cuQBT6t0+vKqb18iryt80VKetbHN95gEX1AxPBh6cPXs206ZNaz5hB1UqFRYvXkxfXx89PT2djtO0bso/o7+5PTG9E4Kz9t/IGUsnMLhRm39BF3H2zumm/Hf1z2mqfzd9XpuVY/a1axvW7k10cyFfVTzvwqZ3VNuZtJ+82mdrSVNLV5bbGbip0YQjYhAYrA5X98/39PRk8wsuyzk7dEf+wQ2t/VMd3KiWX9tpzt453ZC/1c9cN3xeW5VT9uHm7OZCvoxUqPuA2wEkbQ28Bjil6HMb6f7ofcCVRZ9dgRmkI95bMv3Ua1sO3cjyc45o+zTNzMw6WsglbUe6XnvVXpL2A1ZHxO8lXQicLuk+4D7gdGAAmA8QEWskfQk4X9JjwGrgPOBO4PoxWxAzM7MO6fQa+f7AD2uGq/utvwwcB5wLbAN8DpgK3AocHhHral7zAeBp0hr5NsD3geMiYsOoJjczM+sCnT6PfAnP3Aq13vgA+otHoz7rgfcVDzMzs3Gl02vkZmbWIc0eD9Q7MTj3wHSGR6MD9Xw80Njr9CVazczMbARcyM3MzDLmQm5mZpYxF3IzM7OMuZCbmZllzIXczMwsYy7kZmZmGfN55Jal0bgevplZjrxGbmZmljEXcjMzs4y5kJuZmWXMhdzMzCxjLuRmZmYZcyE3MzPLmAu5mZlZxlzIzczMMuZCbmZmljEXcjMzs4y5kJuZmWXMhdzMzCxjvmnKGBmNm3wsP+eItk/TzMzy4jVyMzOzjLmQm5mZZcyF3MzMLGMu5GZmZhlzITczM8uYC7mZmVnGXMjNzMwy5kJuZmaWMV8QJmPTT72W3onBuQfCjP6FDG7QiKfpi8yYmeWlq9fIJfVLitJjVc14FX1WSHpS0hJJL+1kZjMzs7HU1YW8cDewa83jZTXjTgY+CLwXOABYBSyWNHmsQ5qZmXVCDpvWn46IVeVGSQJOAs6OiKuKtrcDDwNzgUvHMqSZmVkn5LBG/sJi0/kySV+X9JdF+17ALsCiaseIGARuAGZ1IKeZmdmY6/Y18luBtwG/AZ4LfAS4qdgPvkvR5+HSax4G9hxqopJ6gd6apskAlUqFSqVC78RoQ/Sx0TshNnkeqUql0pbpNDu/ZufbDb+jdr/3Y8nZOyfn/MPJPtb/Q4ar1f81nTTcrIrI549J0rbAb4FzgVuAG4HdImJlTZ9/B/aIiNcNMZ1+4Mxy+/z585k0aVK7Y5uZmTVtYGCAuXPnAkyJiLWN+nX7GvkmIuIJSXcCLwSuLpp3AVbWdNuZZ6+ll80DLqgZngw8OHv2bKZNm8aM/oVtSjz6eicEZ+2/kTOWTmBw48hPPxsNd/XPaTiuUqmwePFi+vr66OnpGfY0u+F3lMN734izd07O+YeTfajPeye1+r+mk9aubVi7N5FVIS82ib8E+DGwjHSUeh9wezF+a+A1wClDTafYlz5YM10Aenp66Onpacv52GNtcKO6NvcLz1jUcFz1PPiXn/2DJvN3z7J283u/Oc7eOTnnHyp7txfJ6v/5HAw3Z1cXcknnAdcAvyetaX8E2B74ckSEpAuB0yXdB9wHnA4MAPM7k9jMzGxsdXUhB3YHvgb8BfAH0n7xgyLi/mL8ucA2wOeAqaSD4w6PiHUdyGpmZjbmurqQR8TRmxkfQH/xMDMzG3dyOI/czMzMGujqNXIzM8vL9FOvbfs0fTOnoXmN3MzMLGMu5GZmZhlzITczM8uYC7mZmVnGXMjNzMwy5kJuZmaWMRdyMzOzjLmQm5mZZcyF3MzMLGMu5GZmZhlzITczM8uYC7mZmVnGXMjNzMwy5kJuZmaWMRdyMzOzjLmQm5mZZcyF3MzMLGMu5GZmZhnbqtMBzMzMhjL91GtHPI3eicG5B8KM/oUMbhDLzzmiDcm6g9fIzczMMuZCbmZmljEXcjMzs4y5kJuZmWXMhdzMzCxjLuRmZmYZcyE3MzPLmAu5mZlZxlzIzczMMuZCbmZmlrEt5hKtkt4DfAjYFbgbOCkiftzZVGZm1o3acdnXsk5d9nWLWCOXdBRwIXA28HLgx8B1kp7fyVxmZmajbUtZI/8g8KWI+I9i+CRJc4ATgdM6F8vMzMaLdq/lbxwcGFa/7NfIJW0NzAQWlUYtAmaNfSIzM7OxsyWskf8FMBF4uNT+MLBLvRdI6gV6a5omA6xevRqArZ5+ou0hR8tWG4OBgY1sVZnAho3qdJym5Zzf2Tsj5+yQd35nH1sbnx7eGrkiYpSjjC5JuwEPAbMi4uaa9g8Dx0bEi+u8ph84c8xCmpmZtW73iHio0cgtYY38UWADz1773plnr6VXzQMuqBmeDDwI7A6sa3fAUZZzdsg7v7N3Rs7ZIe/8zj72JgMrhuqQfSGPiKck3Qb0Ad+qGdUHfLvBawaBweqw9OfNLOsiYu0oRR0VOWeHvPM7e2fknB3yzu/sHbHZrNkX8sIFwBWSlgI3A+8Cng98oaOpzMzMRtkWUcgj4huSpgEfJV0Q5i7g9RFxf2eTmZmZja4topADRMTngM+1+PJB4GPUbG7PSM7ZIe/8zt4ZOWeHvPM7exfK/qh1MzOz8Sz7C8KYmZmNZy7kZmZmGXMhNzMzy5gLuZmZWcbGfSGX9B5JyyStl3SbpFd1QabTJP1M0jpJj0i6WtLepT6S1C9phaQnJS2R9NJSn15JF0l6VNITkr4jafcOLEtIujCH7JKeJ+mrkh6TNCDpDkkzM8m+laRPFH/PT0r6naSPSppQ06cr8kt6taRrihwh6S2l8W3JKWmqpCskrSkeV0jaYTTzS+qR9ClJdxa5Vkj6itLlpDuef3PvfanvpUWfk3LJLuklRZ41Sv9Db1HNLa07+XczaiJi3D6Ao4CngH8GXkK6p/mfgOd3ONf3gOOAlwL7AguA+4Fta/qcQrriz1uBGcDXSZfxm1zT5/OkSxIeRrpP+w+AO4CJY7QcBwDLgF8AF3Z7dmAqsBy4DDgQmA4cCvxVt2cv5vth0iWLjyiy/x3pUpTv77b8wN8AnyhyBPCW0vi25ASuA+4EDi4edwLXjGZ+YAqwGPgHYG/gIOAWYGlpGh3Jv7n3vqbfW4o8DwEn5ZAd+CvgMeDcItdfFp+HnTudfTQfHQ/Q0YWHW4HPl9ruAeZ1Olsp007FH+2ri2EBK4FTavr0Ao8DJxTDU0hfUo6q6bMb6br0c8Yg83bAb4oPyxKKQt7N2YFzgB8PMb5rsxfzWQB8qdT2TeCKbs5f/ofcrpykL+cB/HVNn4OKtr1HK3+DPgcU/Z7fTfkbZQeeRyp2LyV9uT2pZlzXZid94btiiNd0RfZ2P8btpnXldR/zKcXz6uJ5L9JNYv6cPdL142/gmewzgZ5SnxWkq96NxfJdAlwbEdeX2rs5+5uApZL+W2mXxu2S3plJdoCfAIdKehGApH2BVwLfzSR/VbtyHgysiYhba/rcAqxh7D/jU0iF4PFiuGvzK+2KuQL4dETcXadLV2Yvch8B/EbSwuIzfGtp83tXZh+pcVvIaeE+5p0gSaRryf8kIu4qmqv5hsq+C/BURPxxiD6jQtLRpA/MaXVGd3P2vwROBO4D5pCu1f9ZSW+ryVXN0ShXx9534FPA14BfS6oAt5O2hHytJls1S6Nsncxf1a6cuwCP1Jn+I4zhZ1zSc0hbe+bHMzfr6Ob8pwBPA59tML5bs+9M2hJ4Kmn35OGkG2ldJek1Nbm6MfuIbDGXaB2B8qXtVKetky4G9iGtWZW1kn1Ul0/SHsC/AYdHxPohunZddtIX26URcXoxfHtxgNWJwFdq+nVjdkjHfBwDzAXuBvYDLpS0IiK+XNOvW/OXtSNnvf5jtiySekibeycA7xnOS+hgfqUDO98PvCKKbcrNvJzOvvfVFdNvR8Rnip/vkDQLeDdpi04jnc4+IuN5jbyV+5iPKUkXkTb3zo6IB2tGrSqeh8q+Ctha0tQh+oyGmcU8bpP0tKSngdcA/6f4uTrvbsy+EvhVqe0e0p30qrmgO7MDfBo4JyK+HhF3RsQVwGd4ZstIt+evalfOVcBz60x/J8ZgWYoifiVpV0FfbHrrzG7N/6oiw+9rPr97AudLWl6TqxuzP0rakrC5z3A3Zh+RcVvII+IpoHof81p9wE1jn+gZSi4mHZn52ohYVuqyjPTH1lfzmq1JBbOa/TagUuqzK+kI4NFcvu8DLyOtDVYfS4H/Kn7+XRdnv5F0lHGtF5HOGIDuft8BJgEbS20beOZz3u35q9qV82ZgiqQDa/r8NWl/9aguS00RfyFwWEQ8VurSrfmvIG0B3K/msYL0JXFON2cv/qf/jKE/w12ZfcQ6fbRdJx88c/rZP5GOVPwM6fSzPTuc63Okg2JeQ1orqT62qelzStHnSNIf4Xzqn57zAOkUqpeTiuwdjNHpZzU5lvDs08+6LjvpyOIKcDrwAtIm6ieA/93t2Yv5Xk460rh6+tmRwB+AT3VbftK+zP2KRwAfKH5+fjtzkk4j+gXpqOODgF/SntPPGuYn7bL8dpFtXzb9DG/d6fybe+/r9F9O/dPPui578ffyFPBO0mf4vaS19Fd2OvtoPjoeoNMP0n6r5aRb291GcYpXhzNFg8dxNX0E9JM2B68n7f+ZUZrOc4CLSOdVDgDXAHt0YHmWsGkh79rswBtI54yuJ22Se2dpfDdnn0y6FsL9wJPAb0nn3G7dbfmBQxr8jV/ezpzAjsBXSeekry1+3mE085O+RDX6DB/S6fybe+/r9F/Oswt512YnrZjdV3wG7gDe3A3ZR/Ph25iamZllbNzuIzczM9sSuJCbmZllzIXczMwsYy7kZmZmGXMhNzMzy5gLuZmZWcZcyM3MzDLmQm42zkhaIunCTucws/bwBWHMxhlJOwKViFjX6SxmNnIu5GZmZhnzpnWzcaZ207qk5ZJOl/SfktZJ+r2kd5X67y7p65JWS3pC0tLiblDV8SdK+q2kpyTdK+nY0utD0gmSFkgakHSPpIMlvaDI8oSkmyX9Vel1b5R0m6T1kn4n6UxJW43iW2OWJRdyM/tX0q1mX066897nJb0YQNJ2pBuW7Aa8iXQ3r3Mp/ndIOhL4N+B80l3KLgUukzS7NI8zgK+Q7lT1a9LdzC4F5gH7F30urnaWNId0o4rPAv8LOAE4DvhwuxbabEvhTetm44ykJcAdEXGSpOXAjyPi2GKcSPcCPzMivlCsnZ8HTI+I1XWmdSNwd0S8q6btSmDbiDiiGA7gExFxRjF8EOmez++IiP8s2o4GLouIbYrhHwHXRcS8mukeA5wbEbu19x0xy5vXyM3sl9UfIn2zXwXsXDTtB9xer4gXXgLcWGq7sWivOw/g4eL5zlLbcyRtXwzPBD4q6U/VB/DvwK6SJm1+kczGD+9vMrNKaTh45kv+k8N4fXmznuq01c4jhmibUPN8JnBVnfmtH0Yms3HDa+RmNpRfAvsVp6zVcw/wylLbrKJ9JH4O7B0R/1PnsXGE0zbboniN3MyG8jXgdOBqSacBK0kHxa2IiJuBTwNXSvo58H3gjcBbgcNGON+PAwskPQD8N7AR2Ad4WUR8ZITTNtuieI3czBqKiKeAw4FHgO+S9mufCmwoxl8NvB/4EHA36ejy4yNiyQjnuxB4A9AH/Ay4BfggcP9Ipmu2JfJR62ZmZhnzGrmZmVnGXMjNzMwy5kJuZmaWMRdyMzOzjLmQm5mZZcyF3MzMLGMu5GZmZhlzITczM8uYC7mZmVnGXMjNzMwy5kJuZmaWMRdyMzOzjP1/RzWclZ/3H1EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用pandas自带hist绘制直方图\n",
    "# 出租车一天的收益情况， 从收益中可以看出\n",
    "import matplotlib.pyplot as plt\n",
    "fig     = plt.figure(1,(12,3),dpi = 100)    \n",
    "ax1      = plt.subplot(121)\n",
    "plt.sca(ax1)\n",
    "df0['income'].hist(ax = ax1,bins = 20)\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('income')\n",
    "plt.xlim(0,df0['income'].quantile(1))\n",
    "plt.title('Histogram of income')\n",
    "\n",
    "#图是深圳司机的收入分布，可以看到日收入在1000到1200 的人员较多。从我们的数据集分析得出，出租车司机工薪比较高。如果单从收入方面考虑，做司机可以一试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Shour</th>\n",
       "      <th>taxi_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>3840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>3677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>4131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>3821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>3505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>3741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>4321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>4133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>3657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>3619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>3082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>3919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>4470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>4528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>4604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>3145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Shour  taxi_id\n",
       "0       0     4584\n",
       "1       1     2342\n",
       "2       2     1707\n",
       "3       3     1218\n",
       "4       4      882\n",
       "5       5      771\n",
       "6       6     1279\n",
       "7       7     2379\n",
       "8       8     3840\n",
       "9       9     3677\n",
       "10     10     4131\n",
       "11     11     3821\n",
       "12     12     3505\n",
       "13     13     3741\n",
       "14     14     4321\n",
       "15     15     4133\n",
       "16     16     3657\n",
       "17     17     3619\n",
       "18     18     3082\n",
       "19     19     3919\n",
       "20     20     4470\n",
       "21     21     4528\n",
       "22     22     4604\n",
       "23     23     3145"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table5['Shour'] = pd.to_datetime(table5['time']).apply(lambda r:r.hour)\n",
    "# 每小时订单数\n",
    "df3 = table5.groupby(['Shour'])['taxi_id'].count().reset_index()\n",
    "# 每小时平均订单价格\n",
    "df2 = table5.groupby(['Shour'])['price_per_minutes'].mean().reset_index()\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8点到12点的单子比较多\n",
    "# 22点后的车费比较高\n",
    "fig     = plt.figure(1,(12,3),dpi = 100)    \n",
    "#什么时候接客订单多？什么时候接客单笔订单价格高\n",
    "#创建一个子图\n",
    "ax1      = plt.subplot(121)\n",
    "plt.sca(ax1)\n",
    "\n",
    "#绘制折线图\n",
    "plt.plot(df3['Shour'],df3['taxi_id'],label = 'count')\n",
    "plt.legend()\n",
    "plt.title('Num. of orders per hour')\n",
    "plt.xticks(range(24),df3['Shour'])\n",
    "plt.ylim(0,5000)\n",
    "#创建另一个子图\n",
    "ax2      = plt.subplot(122)\n",
    "plt.sca(ax2)\n",
    "\n",
    "#绘制折线图\n",
    "plt.plot(df3['Shour'],df2['price_per_minutes'],label = 'price')\n",
    "plt.legend()\n",
    "plt.title('Average price per order')\n",
    "plt.xticks(range(24),df3['Shour'])\n",
    "plt.ylim(0,3)\n",
    "plt.show()\n",
    "#每小时的订单量的展示图：结论是8点到22点的订单量比较多。可以到这时间段上班，没必要起早床，这样赚钱效率不高，还不如睡觉。\n",
    "#每笔订单的价格图：结论是追求单笔高价格订单的可以选择22点到23点之间去。看司机属于喜欢多订单低价格方式还是低订单高价格方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "464\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5       22242\n",
       "18      22307\n",
       "22      22317\n",
       "23      22321\n",
       "27      22345\n",
       "        ...  \n",
       "2299    36787\n",
       "2300    36793\n",
       "2310    36856\n",
       "2312    36859\n",
       "2315    36899\n",
       "Name: taxi_id, Length: 464, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#收入最高的车手和收入中等的车手的挣钱方式的区别\n",
    "\n",
    "highincome = df0[df0['income']>df0['income'].quantile(0.8)]['taxi_id']\n",
    "print(len(highincome))\n",
    "highincome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "464"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midincome = df0[(df0['income']>df0['income'].quantile(0.4))&(df0['income']<df0['income'].quantile(0.6))]['taxi_id']\n",
    "len(midincome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "464"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smallincome= df0[(df0['income']<df0['income'].quantile(0.2))]['taxi_id']\n",
    "len(smallincome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "highincome_order = pd.merge(table5,highincome,on = 'taxi_id')\n",
    "midincome_order = pd.merge(table5,midincome,on = 'taxi_id')\n",
    "smallincome_order = pd.merge(table5,smallincome,on = 'taxi_id')\n",
    "\n",
    "\n",
    "highincome_order.to_csv(r'highincome_order.csv',index = None)\n",
    "midincome_order.to_csv(r'midincome_order.csv',index = None)\n",
    "smallincome_order.to_csv(r'smallincome_order.csv',index = None)"
   ]
  },
  {
   "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 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>orderid</th>\n",
       "      <th>taxi_id</th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>isd</th>\n",
       "      <th>Etime</th>\n",
       "      <th>ELng</th>\n",
       "      <th>ELat</th>\n",
       "      <th>distance</th>\n",
       "      <th>...</th>\n",
       "      <th>isnight</th>\n",
       "      <th>起步价</th>\n",
       "      <th>里程价</th>\n",
       "      <th>返空费</th>\n",
       "      <th>夜间附加费</th>\n",
       "      <th>候时费</th>\n",
       "      <th>price</th>\n",
       "      <th>speed</th>\n",
       "      <th>price_per_minutes</th>\n",
       "      <th>Shour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>179.0</td>\n",
       "      <td>22242</td>\n",
       "      <td>2021-05-18 00:15:33</td>\n",
       "      <td>22.650642</td>\n",
       "      <td>114.009781</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-18 23:16:20</td>\n",
       "      <td>114.041870</td>\n",
       "      <td>22.603216</td>\n",
       "      <td>38630.306848</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>87.912736</td>\n",
       "      <td>9.813821</td>\n",
       "      <td>17.582547</td>\n",
       "      <td>13.506667</td>\n",
       "      <td>144.815771</td>\n",
       "      <td>1.678626</td>\n",
       "      <td>0.104879</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>180.0</td>\n",
       "      <td>22242</td>\n",
       "      <td>2021-05-18 00:16:01</td>\n",
       "      <td>22.650581</td>\n",
       "      <td>114.009811</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-18 01:45:36</td>\n",
       "      <td>114.121513</td>\n",
       "      <td>22.567055</td>\n",
       "      <td>20536.317434</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>44.487162</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>8.897432</td>\n",
       "      <td>1.386667</td>\n",
       "      <td>70.771261</td>\n",
       "      <td>13.754557</td>\n",
       "      <td>0.790005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>181.0</td>\n",
       "      <td>22242</td>\n",
       "      <td>2021-05-18 01:45:39</td>\n",
       "      <td>22.567144</td>\n",
       "      <td>114.121613</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-18 02:09:02</td>\n",
       "      <td>114.132225</td>\n",
       "      <td>22.565376</td>\n",
       "      <td>8326.728763</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>15.184149</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>3.036830</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>34.540979</td>\n",
       "      <td>21.365804</td>\n",
       "      <td>1.477162</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>182.0</td>\n",
       "      <td>22242</td>\n",
       "      <td>2021-05-18 02:09:09</td>\n",
       "      <td>22.565453</td>\n",
       "      <td>114.132195</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-18 03:37:23</td>\n",
       "      <td>114.092682</td>\n",
       "      <td>22.555117</td>\n",
       "      <td>17549.642653</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>37.319142</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>7.463828</td>\n",
       "      <td>0.306667</td>\n",
       "      <td>61.089638</td>\n",
       "      <td>11.934022</td>\n",
       "      <td>0.692365</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>183.0</td>\n",
       "      <td>22242</td>\n",
       "      <td>2021-05-18 03:37:49</td>\n",
       "      <td>22.555000</td>\n",
       "      <td>114.092644</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-18 06:03:29</td>\n",
       "      <td>113.809052</td>\n",
       "      <td>22.627281</td>\n",
       "      <td>36288.342721</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>82.292023</td>\n",
       "      <td>8.127607</td>\n",
       "      <td>16.458405</td>\n",
       "      <td>1.786667</td>\n",
       "      <td>124.664700</td>\n",
       "      <td>14.947143</td>\n",
       "      <td>0.855822</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22816</th>\n",
       "      <td>96139.0</td>\n",
       "      <td>36899</td>\n",
       "      <td>2021-05-18 21:03:33</td>\n",
       "      <td>22.567533</td>\n",
       "      <td>114.125214</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-18 21:24:59</td>\n",
       "      <td>114.137283</td>\n",
       "      <td>22.621099</td>\n",
       "      <td>9768.451574</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>11</td>\n",
       "      <td>18.644284</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>30.444284</td>\n",
       "      <td>27.345588</td>\n",
       "      <td>1.420418</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22817</th>\n",
       "      <td>96140.0</td>\n",
       "      <td>36899</td>\n",
       "      <td>2021-05-18 21:25:06</td>\n",
       "      <td>22.621149</td>\n",
       "      <td>114.137383</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-18 22:07:00</td>\n",
       "      <td>114.079483</td>\n",
       "      <td>22.536100</td>\n",
       "      <td>15703.845343</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>11</td>\n",
       "      <td>32.889229</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.106667</td>\n",
       "      <td>50.995895</td>\n",
       "      <td>22.487607</td>\n",
       "      <td>1.217086</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22818</th>\n",
       "      <td>96141.0</td>\n",
       "      <td>36899</td>\n",
       "      <td>2021-05-18 22:07:06</td>\n",
       "      <td>22.536100</td>\n",
       "      <td>114.079483</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-18 22:33:25</td>\n",
       "      <td>114.102333</td>\n",
       "      <td>22.552549</td>\n",
       "      <td>6121.373629</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>11</td>\n",
       "      <td>9.891297</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.866667</td>\n",
       "      <td>26.757963</td>\n",
       "      <td>13.956267</td>\n",
       "      <td>1.016769</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22819</th>\n",
       "      <td>96142.0</td>\n",
       "      <td>36899</td>\n",
       "      <td>2021-05-18 22:33:32</td>\n",
       "      <td>22.552567</td>\n",
       "      <td>114.102417</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-18 23:24:51</td>\n",
       "      <td>113.962685</td>\n",
       "      <td>22.547382</td>\n",
       "      <td>30801.829547</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>11</td>\n",
       "      <td>69.124391</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.933333</td>\n",
       "      <td>83.057724</td>\n",
       "      <td>36.013831</td>\n",
       "      <td>1.618533</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22820</th>\n",
       "      <td>96143.0</td>\n",
       "      <td>36899</td>\n",
       "      <td>2021-05-18 23:25:06</td>\n",
       "      <td>22.547382</td>\n",
       "      <td>113.962685</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-18 23:41:51</td>\n",
       "      <td>113.978416</td>\n",
       "      <td>22.562517</td>\n",
       "      <td>4897.508353</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>6.954020</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>1.390804</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>27.544824</td>\n",
       "      <td>17.543314</td>\n",
       "      <td>1.644467</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22821 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       orderid  taxi_id                 time        lat         lon  isd  \\\n",
       "0        179.0    22242  2021-05-18 00:15:33  22.650642  114.009781    0   \n",
       "1        180.0    22242  2021-05-18 00:16:01  22.650581  114.009811    0   \n",
       "2        181.0    22242  2021-05-18 01:45:39  22.567144  114.121613    0   \n",
       "3        182.0    22242  2021-05-18 02:09:09  22.565453  114.132195    0   \n",
       "4        183.0    22242  2021-05-18 03:37:49  22.555000  114.092644    0   \n",
       "...        ...      ...                  ...        ...         ...  ...   \n",
       "22816  96139.0    36899  2021-05-18 21:03:33  22.567533  114.125214    0   \n",
       "22817  96140.0    36899  2021-05-18 21:25:06  22.621149  114.137383    0   \n",
       "22818  96141.0    36899  2021-05-18 22:07:06  22.536100  114.079483    0   \n",
       "22819  96142.0    36899  2021-05-18 22:33:32  22.552567  114.102417    0   \n",
       "22820  96143.0    36899  2021-05-18 23:25:06  22.547382  113.962685    0   \n",
       "\n",
       "                     Etime        ELng       ELat      distance  ...  isnight  \\\n",
       "0      2021-05-18 23:16:20  114.041870  22.603216  38630.306848  ...     True   \n",
       "1      2021-05-18 01:45:36  114.121513  22.567055  20536.317434  ...     True   \n",
       "2      2021-05-18 02:09:02  114.132225  22.565376   8326.728763  ...     True   \n",
       "3      2021-05-18 03:37:23  114.092682  22.555117  17549.642653  ...     True   \n",
       "4      2021-05-18 06:03:29  113.809052  22.627281  36288.342721  ...     True   \n",
       "...                    ...         ...        ...           ...  ...      ...   \n",
       "22816  2021-05-18 21:24:59  114.137283  22.621099   9768.451574  ...    False   \n",
       "22817  2021-05-18 22:07:00  114.079483  22.536100  15703.845343  ...    False   \n",
       "22818  2021-05-18 22:33:25  114.102333  22.552549   6121.373629  ...    False   \n",
       "22819  2021-05-18 23:24:51  113.962685  22.547382  30801.829547  ...    False   \n",
       "22820  2021-05-18 23:41:51  113.978416  22.562517   4897.508353  ...     True   \n",
       "\n",
       "       起步价        里程价       返空费      夜间附加费        候时费       price      speed  \\\n",
       "0       16  87.912736  9.813821  17.582547  13.506667  144.815771   1.678626   \n",
       "1       16  44.487162 -0.000000   8.897432   1.386667   70.771261  13.754557   \n",
       "2       16  15.184149 -0.000000   3.036830   0.320000   34.540979  21.365804   \n",
       "3       16  37.319142 -0.000000   7.463828   0.306667   61.089638  11.934022   \n",
       "4       16  82.292023  8.127607  16.458405   1.786667  124.664700  14.947143   \n",
       "...    ...        ...       ...        ...        ...         ...        ...   \n",
       "22816   11  18.644284 -0.000000   0.000000   0.800000   30.444284  27.345588   \n",
       "22817   11  32.889229 -0.000000   0.000000   7.106667   50.995895  22.487607   \n",
       "22818   11   9.891297 -0.000000   0.000000   5.866667   26.757963  13.956267   \n",
       "22819   11  69.124391  0.000000   0.000000   2.933333   83.057724  36.013831   \n",
       "22820   16   6.954020 -0.000000   1.390804   3.200000   27.544824  17.543314   \n",
       "\n",
       "       price_per_minutes  Shour  \n",
       "0               0.104879      0  \n",
       "1               0.790005      0  \n",
       "2               1.477162      1  \n",
       "3               0.692365      2  \n",
       "4               0.855822      3  \n",
       "...                  ...    ...  \n",
       "22816           1.420418     21  \n",
       "22817           1.217086     21  \n",
       "22818           1.016769     22  \n",
       "22819           1.618533     22  \n",
       "22820           1.644467     23  \n",
       "\n",
       "[22821 rows x 21 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highincome_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.07327798307587"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highincome_money=highincome_order['price'].sum()/len(highincome_order)\n",
    "highincome_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.57742437463463"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lowincome_money=smallincome_order['price'].sum()/len(smallincome_order)\n",
    "lowincome_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#高收入中收入群体每天的订单数量,  高收入群体主要是靠勤奋而赚钱的\n",
    "import seaborn as sns\n",
    "fig     = plt.figure(1,(7,3),dpi = 100)    \n",
    "ax      = plt.subplot(121)\n",
    "plt.sca(ax)\n",
    "# plt.boxplot([highincome_order.groupby('taxi_id')['orderid'].count(),\n",
    "# midincome_order.groupby('taxi_id')['orderid'].count()])\n",
    "\n",
    "plt.boxplot([highincome_order.groupby('taxi_id')['orderid'].count(),\n",
    "smallincome_order.groupby('taxi_id')['orderid'].count()])\n",
    "plt.xticks([1,2],['High income','low income'])\n",
    "plt.ylabel('Num. of orders')\n",
    "\n",
    "\n",
    "#订单每分钟收入分布情况\n",
    "  \n",
    "ax= plt.subplot(122)\n",
    "plt.sca(ax)\n",
    "# plt.boxplot([highincome_order['price_per_minutes'],\n",
    "# midincome_order['price_per_minutes']])\n",
    "\n",
    "plt.boxplot([highincome_order['price_per_minutes'],\n",
    "smallincome_order['price_per_minutes']])\n",
    "\n",
    "plt.xticks([1,2],['High income','low income'])\n",
    "plt.ylim(-0.1,4)\n",
    "plt.ylabel('Price per minutes')\n",
    "plt.show()\n",
    "#高收入群体是前20%， 中等收入群体是40%到60%。低收入群体是最后的20%。可以观察收入高的司机是凭借接单的数目获胜。而不是每分钟赚钱的效率获胜"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#统计高收入者和低收入者每小时平均的订单数量,差别不大才怪。 说明成功人士的努力程度也毫不逊色。\n",
    "high_hour =  (highincome_order.groupby(pd.to_datetime(highincome_order['time']\n",
    "                  ).apply(lambda r:r.hour))['price'].count()\n",
    "/len(highincome)).reset_index().rename(columns = {'price':'avg_count'})\n",
    "high_hour['Index'] = 'High income'\n",
    "\n",
    "\n",
    "\n",
    "low_hour =  (smallincome_order.groupby(pd.to_datetime(smallincome_order['time']\n",
    "                  ).apply(lambda r:r.hour))['price'].count()\n",
    "/len(smallincome)).reset_index().rename(columns = {'price':'avg_count'})\n",
    "low_hour['Index'] = 'Low income'\n",
    "#绘图\n",
    "fig     = plt.figure(1,(10,5),dpi = 100)    \n",
    "ax      = plt.subplot(111)\n",
    "plt.sca(ax)\n",
    "sns.lineplot(x=\"time\", y=\"avg_count\",hue=\"Index\", data=pd.concat([high_hour,low_hour]),ax = ax)\n",
    "plt.ylabel('avg order num per driver')\n",
    "plt.xlabel('Order time')\n",
    "plt.ylim(0,4)\n",
    "plt.xticks(range(24),range(24))\n",
    "plt.show()\n",
    "# 可以看出高收入司机群体无时无刻的单子数都要多。楷模典范"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "清洗OpenStatus异常的数据前 9843421\n",
      "清洗OpenStatus异常的数据后 9825212\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>taxi_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22224.0</td>\n",
       "      <td>471885.042524</td>\n",
       "      <td>353.913782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22228.0</td>\n",
       "      <td>530294.725281</td>\n",
       "      <td>397.721044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22231.0</td>\n",
       "      <td>546072.581893</td>\n",
       "      <td>409.554436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22236.0</td>\n",
       "      <td>301676.002672</td>\n",
       "      <td>226.257002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22240.0</td>\n",
       "      <td>3109.042075</td>\n",
       "      <td>2.331782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>36934.0</td>\n",
       "      <td>538403.703891</td>\n",
       "      <td>403.802778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>36936.0</td>\n",
       "      <td>415133.365065</td>\n",
       "      <td>311.350024</td>\n",
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       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>36941.0</td>\n",
       "      <td>424744.270417</td>\n",
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       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>36946.0</td>\n",
       "      <td>441262.366927</td>\n",
       "      <td>330.946775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>36947.0</td>\n",
       "      <td>526367.176189</td>\n",
       "      <td>394.775382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      taxi_id       distance        cost\n",
       "0     22224.0  471885.042524  353.913782\n",
       "1     22228.0  530294.725281  397.721044\n",
       "2     22231.0  546072.581893  409.554436\n",
       "3     22236.0  301676.002672  226.257002\n",
       "4     22240.0    3109.042075    2.331782\n",
       "...       ...            ...         ...\n",
       "2995  36934.0  538403.703891  403.802778\n",
       "2996  36936.0  415133.365065  311.350024\n",
       "2997  36941.0  424744.270417  318.558203\n",
       "2998  36946.0  441262.366927  330.946775\n",
       "2999  36947.0  526367.176189  394.775382\n",
       "\n",
       "[3000 rows x 3 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算出租车行驶的距离\n",
    "#读取原始数据\n",
    "data = pd.read_csv(r'sample_taxi.csv')\n",
    "\n",
    "\n",
    "data = data.sort_values(by = ['taxi_id', 'time'])\n",
    "\n",
    "#将时间字符串转换为pd的时间格式，后面可以轻松的做加减\n",
    "data['time'] = pd.to_datetime(data['time'])\n",
    "\n",
    "\n",
    "\n",
    "########清洗OpenStatus异常的数据#########\n",
    "print('清洗OpenStatus异常的数据前',len(data))\n",
    "data = data[-((data['is_passenger'].shift(-1) == data['is_passenger'].shift())&\n",
    "(data['is_passenger'].shift(-1) != data['is_passenger'])&\n",
    "(data['taxi_id'].shift(-1) == data['taxi_id'].shift())&\n",
    "(data['taxi_id'].shift(-1) == data['taxi_id']))]\n",
    "print('清洗OpenStatus异常的数据后',len(data))\n",
    "#######################################\n",
    "\n",
    "#定义计算路径长度\n",
    "data['lon1'] = data['lon'].shift(-1)\n",
    "data['lat1'] = data['lat'].shift(-1)\n",
    "data['time1'] = data['time'].shift(-1)\n",
    "data['taxi_id'] = data['taxi_id'].shift(-1)\n",
    "\n",
    "\n",
    "#计算每个点与下一个点的距离\n",
    "lon1 = data['lon']\n",
    "lat1 = data['lat']\n",
    "lon2 = data['lon1']\n",
    "lat2 = data['lat1']\n",
    "data['distance'] = getdistance(lon1, lat1, lon2, lat2)\n",
    "\n",
    "#计算每个点与下一个点的时间差\n",
    "data['interval'] = (data['time1']-data['time']).apply(lambda r:r.seconds)\n",
    "\n",
    "#速度\n",
    "data['speed'] = (data['distance']/data['interval'])*3.6\n",
    "\n",
    "data = data[data['taxi_id'] == data['taxi_id']]\n",
    "\n",
    "#计算每辆车的驾驶距离\n",
    "disagg = data.groupby(['taxi_id'])['distance'].sum().reset_index()\n",
    "disagg.head(5)\n",
    "disagg\n",
    "\n",
    "#小汽车每百公里大概在35-75元左右不等，我们统一定为每公里0.75元油费\n",
    "disagg['cost'] = (disagg['distance']/1000)*0.75\n",
    "disagg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>taxi_id</th>\n",
       "      <th>is_passenger</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22224.0</td>\n",
       "      <td>0</td>\n",
       "      <td>86235.645202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22224.0</td>\n",
       "      <td>1</td>\n",
       "      <td>385649.397322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22228.0</td>\n",
       "      <td>0</td>\n",
       "      <td>99315.025879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22228.0</td>\n",
       "      <td>1</td>\n",
       "      <td>430979.699402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22231.0</td>\n",
       "      <td>0</td>\n",
       "      <td>203005.997854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   taxi_id  is_passenger       distance\n",
       "0  22224.0             0   86235.645202\n",
       "1  22224.0             1  385649.397322\n",
       "2  22228.0             0   99315.025879\n",
       "3  22228.0             1  430979.699402\n",
       "4  22231.0             0  203005.997854"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算每辆车的空驶距离和载客距离\n",
    "disagg = data.groupby(['taxi_id','is_passenger'])['distance'].sum().reset_index()\n",
    "disagg.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#整理数据到一个DataFrame中\n",
    "highagg = pd.merge(highincome,disagg, on = 'taxi_id')\n",
    "highagg['Index'] = 'High income'\n",
    "highagg['distance'] = highagg['distance']/1000\n",
    "midagg = pd.merge(midincome,disagg, on = 'taxi_id')\n",
    "midagg['Index'] = 'Middle income'\n",
    "midagg['distance'] = midagg['distance']/1000\n",
    "\n",
    "\n",
    "\n",
    "lowagg = pd.merge(smallincome,disagg, on = 'taxi_id')\n",
    "lowagg['Index'] = 'Low income'\n",
    "lowagg['distance'] = lowagg['distance']/1000\n",
    "\n",
    "\n",
    "#绘图\n",
    "fig     = plt.figure(1,(10,5),dpi = 100)    \n",
    "ax      = plt.subplot(111)\n",
    "plt.sca(ax)\n",
    "sns.boxplot(x=\"is_passenger\", y=\"distance\",hue=\"Index\", data=pd.concat([highagg,lowagg])\n",
    ",ax = ax)\n",
    "plt.ylabel('Travel distance $(km)$')\n",
    "plt.ylim(0,700)\n",
    "plt.xticks([0,1],['Idle','Delivery'])\n",
    "plt.show()\n",
    "\n",
    "#空车走过的距离两个群体都不多。这点大家都差不多精致。\n",
    "# 但是高收入群体载客走过的距离要相对多一些。来源可能来自于单子数目多或者单个客人行程远。总而言之，行驶距离更高。所以也是两个群体拉开的差距"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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