计算GPS数据的距离[经度和纬度]

Question

我是pandas datamining的新手。 我有GPS数据集，包括时间戳，经度和纬度值。 我的数据集看起来像这样 。

In [3]:
import pandas as pd
import numpy as np
df = pd.read_csv('D:GPS.csv', index_col=None)
df
Out[3]:
    time                mLongitude  mLongitude
0   2014-06-30 00:00:00 94.500000   126.998428
1   2014-06-30 00:00:00 94.500000   126.998428
2   2014-06-30 00:00:00 94.500000   126.998428
3   2014-06-30 00:00:00 94.500000   126.998428
4   2014-06-30 00:00:00 94.500000   126.998428
5   2014-06-30 00:00:00 94.500000   126.998428
6   2014-06-30 00:00:00 94.500000   126.998428
7   2014-06-30 00:00:00 94.500000   126.998428
8   2014-06-30 00:00:00 94.500000   126.998428
9   2014-06-30 00:00:00 94.500000   126.998428
10  2014-06-30 00:00:00 94.500000   126.998428
11  2014-06-30 00:00:00 94.500000   126.998428
12  2014-06-30 00:00:00 94.500000   126.998428
13  2014-06-30 00:00:00 94.500000   126.998428
14  2014-06-30 00:00:00 94.500000   126.998428
15  2014-06-30 00:00:00 94.500000   126.998428
  ...   ... ... ...
9467    2014-08-02 00:00:00 44.299999   126.902259
9468    2014-08-02 00:00:00 44.299999   126.902259
9469    2014-08-02 00:00:00 44.299999   126.902259
9470    2014-08-02 00:00:00 44.299999   126.902259
9471    2014-08-02 00:00:00 44.299999   126.902259
9472    2014-08-02 00:00:00 44.299999   126.902259

在这里，我想计算每一天的行驶距离。 然后输出的例子是这样的：

 time        distance (meter)
2014-06-30     1000 
2014-07-01     500
....           ...
2014-08-02     1500

Answer 1

以下是根据我的回答改编的：

In [133]:

import math
​
df['distance'] = 6367 * 2 * np.arcsin(np.sqrt(np.sin(np.radians(df['mLatitude']) - math.radians(37.2175900)/2)**2 + math.cos(math.radians(37.2175900)) * np.cos(np.radians(df['mLatitude']) * np.sin(np.radians(df['mLongitude']) - math.radians(-56.7213600)/2)**2)))
df
Out[133]:
            time  mLongitude   mLatitude      distance
index                                                 
0     2014-06-30   94.500000  126.998428  16032.604625
1     2014-06-30   94.500000  126.998428  16032.604625
2     2014-06-30   94.500000  126.998428  16032.604625
3     2014-06-30   94.500000  126.998428  16032.604625
4     2014-06-30   94.500000  126.998428  16032.604625
5     2014-06-30   94.500000  126.998428  16032.604625
6     2014-06-30   94.500000  126.998428  16032.604625
7     2014-06-30   94.500000  126.998428  16032.604625
8     2014-06-30   94.500000  126.998428  16032.604625
9     2014-06-30   94.500000  126.998428  16032.604625
10    2014-06-30   94.500000  126.998428  16032.604625
11    2014-06-30   94.500000  126.998428  16032.604625
12    2014-06-30   94.500000  126.998428  16032.604625
13    2014-06-30   94.500000  126.998428  16032.604625
14    2014-06-30   94.500000  126.998428  16032.604625
15    2014-06-30   94.500000  126.998428  16032.604625
9467  2014-08-02   44.299999  126.902259  10728.740464
9468  2014-08-02   44.299999  126.902259  10728.740464
9469  2014-08-02   44.299999  126.902259  10728.740464
9470  2014-08-02   44.299999  126.902259  10728.740464
9471  2014-08-02   44.299999  126.902259  10728.740464
9472  2014-08-02   44.299999  126.902259  10728.740464
In [137]:

df.set_index('time').resample('D', how='mean')
Out[137]:
            mLongitude   mLatitude      distance
time                                            
2014-06-30   94.500000  126.998428  16032.604625
2014-08-02   44.299999  126.902259  10728.740464

目前还不清楚你的时间是否已经过了日期时间，但如果不是你可以转换它： df['time'] = pd.to_datetime(df['time']) ，我也重新标记了列，因为你有2 mLongitude ，I假设第二个应该是纬度