计算大 pandas 每组的数值差异 [英] Calculating numeric differences per group in pandas

问题描述

我的数据框具有以下结构：

  patient_id |时间戳|测量
 A | 2014-10-10 | 5.7 
 A | 2014-10-11 | 6.3 
 B | 2014-10-11 | 6.1 
 B | 2014-10-10 | 4.1 
  

我想计算一个 delta $ / code> （差异）

结果应如下所示：

  patient_id |时间戳|测量| 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 |在大熊猫中，这样做最为优雅？
 
 
 
 
 $ b 
 div class =h2_lin>解决方案

致电 转换 在测量列上传递方法 diff ，转换返回一个索引与原始df：

 在[4]中：
 
 df ['delta'] = df.groupby （'patient_id'）['measurement']。transform（pd.Series.diff）
 df 
 Out [4]：
 patient_id时间戳测量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

如果您打算对变换的结果应用一些排序，然后先排序df：

 在[10]中：
 
 df ['delta'] = df.sort（columns = ['patient_id'时间戳']）。groupby（'patient_id'）['measurement']。transform（pd.Series.diff）
 df 
输出[10]：
 patient_id时间戳测量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 
  

My Dataframe has the following structure:

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

I would like to calculate a delta (difference) between each measurement of each patient.

The result should look like:

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

How can this be done most-elegantly in pandas ?

解决方案

Call transform on the 'measurement' column and pass the method diff, transform returns a series with an index aligned to the original df:

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

EDIT

If you are intending to apply some sorting on the result of transform then sort the df first:

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

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