计算 pandas 中每组的数值差异

Question

我的 Dataframe 具有以下结构：

patient_id  |  timestamp  |  measurement
A           |  2014-10-10 |  5.7
A           |  2014-10-11 |  6.3
B           |  2014-10-11 |  6.1
B           |  2014-10-10 |  4.1

我想计算每位患者每次测量之间的 delta（差异）。

结果应如下所示：

patient_id  |  timestamp  |  measurement  |    delta
A           |  2014-10-10 |  5.7          |     NaN
A           |  2014-10-11 |  6.3          |     0.6
B           |  2014-10-11 |  6.1          |     2.0
B           |  2014-10-10 |  4.1          |     NaN

如何在 pandas 中最优雅地完成这项工作？

Answer 1

调用transform on the 'measurement' column and pass the method diff，转换returns一个索引与原始df对齐的序列：

In [4]:

df['delta'] = df.groupby('patient_id')['measurement'].transform(pd.Series.diff)
df
Out[4]:
  patient_id   timestamp  measurement  delta
0          A  2014-10-10          5.7    NaN
1          A  2014-10-11          6.3    0.6
2          B  2014-10-10          4.1    NaN
3          B  2014-10-11          6.1    2.0

编辑

如果您打算对 transform 的结果进行排序，那么首先对 df 进行排序：

In [10]:

df['delta'] = df.sort(columns=['patient_id', 'timestamp']).groupby('patient_id')['measurement'].transform(pd.Series.diff)
df
Out[10]:
  patient_id   timestamp  measurement  delta
0          A  2014-10-10          5.7    NaN
1          A  2014-10-11          6.3    0.6
2          B  2014-10-11          6.1    2.0
3          B  2014-10-10          4.1    NaN

计算 pandas 中每组的数值差异

Calculating numeric differences per group in pandas

python

time-series

dataframe

python-2.7

pandas