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我对 Pandas 很陌生，但熟悉 Numpy 和 Python。

假设我有一个按时间（日期时间）索引的 X、Y 点（float64）的“Pandas.DataFrame”，如果我已经知道如何计算点之间的欧几里德距离，我该如何计算速度？

编辑：我刚刚阅读了关于 的帮助pandas.Series.diff()，但我仍然想用另一个函数“替换” diff 上使用的减法，比如“euclidean_distance()”。有没有办法做到这一点？

DataFrame 看起来像（第一列中的索引，第二列中的位置）：

2009-08-07 16:16:44    [37.800185, -122.426361]
2009-08-07 16:16:48    [37.800214, -122.426153]
2009-08-07 16:16:49    [37.800222, -122.426118]
2009-08-07 16:16:52    [37.800197, -122.426072]
2009-08-07 16:17:32    [37.800214, -122.425903]
2009-08-07 16:17:34    [37.800236, -122.425826]
2009-08-07 16:17:40    [37.800282, -122.425534]
2009-08-07 16:17:44    [37.800307, -122.425315]
2009-08-07 16:17:46    [37.800324, -122.425207]
2009-08-07 16:17:47    [37.800331, -122.425153]
2009-08-07 16:17:49    [37.800343, -122.425047]
2009-08-07 16:17:50    [37.800355, -122.424994]
2009-08-07 16:17:51    [37.800362, -122.424942]
2009-08-07 16:17:54    [37.800378, -122.424796]
2009-08-07 16:17:56    [37.800357, -122.424764]

我想要的是某种从中获得速度的方法，提供第一个数据样本的速度根据定义始终为零（前一个样本中没有已知的时间增量）。

非常感谢！

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1 回答 1

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像这样的东西会起作用吗？

In [99]: df
Out[99]: 
                            X         Y
2009-08-07 00:00:00 -0.900602 -1.107547
2009-08-07 01:00:00  0.398914  1.545534
2009-08-07 02:00:00 -0.429100  2.052242
2009-08-07 03:00:00  0.857940 -0.348118
2009-08-07 04:00:00  0.394655 -1.578197
2009-08-07 05:00:00 -0.240995 -1.474097
2009-08-07 06:00:00  0.619148 -0.040635
2009-08-07 07:00:00 -1.403177 -0.187540
2009-08-07 08:00:00 -0.360626 -0.399728
2009-08-07 09:00:00  0.179741 -2.709712

In [100]: df['Time'] = df.index.asi8

In [101]: dist = df.diff().fillna(0.)

In [102]: dist['Dist'] = np.sqrt(dist.X**2 + dist.Y**2)

In [103]: dist['Speed'] = dist.Dist / (dist.Time / 1e9)

In [104]: dist
Out[104]: 
                            X         Y          Time      Dist     Speed
2009-08-07 00:00:00  0.000000  0.000000  0.000000e+00  0.000000       NaN
2009-08-07 01:00:00  1.299516  2.653081  3.600000e+12  2.954248  0.000821
2009-08-07 02:00:00 -0.828013  0.506708  3.600000e+12  0.970752  0.000270
2009-08-07 03:00:00  1.287040 -2.400360  3.600000e+12  2.723637  0.000757
2009-08-07 04:00:00 -0.463285 -1.230079  3.600000e+12  1.314430  0.000365
2009-08-07 05:00:00 -0.635650  0.104100  3.600000e+12  0.644118  0.000179
2009-08-07 06:00:00  0.860143  1.433462  3.600000e+12  1.671724  0.000464
2009-08-07 07:00:00 -2.022324 -0.146906  3.600000e+12  2.027653  0.000563
2009-08-07 08:00:00  1.042550 -0.212188  3.600000e+12  1.063924  0.000296
2009-08-07 09:00:00  0.540367 -2.309984  3.600000e+12  2.372345  0.000659
于 2012-09-08T00:42:02.103 回答