# Mean across every several rows in pandas

I have a table of features and labels where each row has a time stamp. Labels are categorical. They go in a batch where one label repeats several times. Batches with the same label do not have a specific order. The number of repetitions of the same label in one batch is always the same. In the example below, every three rows has the same label.

I would like to get a new table where Var 1 and Var 2 are averaged across repetitions of labels. In this example, I would like to average every three rows as follows:

Is this possible to do with some functions in pandas or other Pyhton libraries?

Solution:

In [24]: res = (df.groupby((df.Label != df.Label.shift()).cumsum())
.mean()
.reset_index(drop=True))


Result:

In [25]: res
Out[25]:
Var1       Var2  Label
0  22.413333  18.733333      2
1  39.390000  20.270000      3
2  38.450000  20.196667      1
3  21.173333  17.860000      3
4  36.453333  19.246667      2


Source DF (I had to use an OCR program in order to parse the data from your picture - please post your dataset in text/CSV form next time):

In [23]: df
Out[23]:
Timestamp   Var1   Var2  Label
0  2015-01-01  23.56  18.85      2
1  2015-02-01  21.23  18.61      2
2  2015-03-01  22.45  18.74      2
3  2015-04-01  35.32  19.94      3
4  2015-05-01  40.50  20.36      3
5  2015-06-01  42.35  20.51      3
6  2015-07-01  41.33  20.43      1
7  2015-08-01  38.35  20.19      1
8  2015-09-01  35.67  19.97      1
9  2015-10-01  22.20  17.97      3
10 2015-11-01  20.11  17.75      3
11 2015-12-01  21.21  17.86      3
12 2015-01-13  32.79  18.95      2
13 2015-01-14  37.45  19.33      2
14 2015-01-15  39.12  19.46      2


Explanation: if we want to group DF by consecutive labels of the same value, then we need to create a series with a unique value for each group. This can be done using the following trick:

In [32]: (df.Label != df.Label.shift()).cumsum()
Out[32]:
0     1
1     1
2     1
3     2
4     2
5     2
6     3
7     3
8     3
9     4
10    4
11    4
12    5
13    5
14    5
Name: Label, dtype: int32

In [33]: df.Label != df.Label.shift()
Out[33]:
0      True
1     False
2     False
3      True
4     False
5     False
6      True
7     False
8     False
9      True
10    False
11    False
12     True
13    False
14    False
Name: Label, dtype: bool


NOTE: False == 0 and True == 1 in Python.

• Thanks! This works even when batches has different sizes! And thanks for the comment on posting text instead of a picture. May I ask you to explain how this part works, please: df.groupby((df.Label != df.Label.shift()).cumsum())? I know what groupby, shift and cumsum do, but cannot figure out why here the summation happens only inside the batches. – Mirit Jan 14 '19 at 13:11
• @Mirit, i've added a short explanation – MaxU Jan 14 '19 at 13:18
• thank you very much! Now everything is working and everything is clear. – Mirit Jan 14 '19 at 13:33
• @Mirit, glad I could help :) – MaxU Jan 14 '19 at 13:33

You can try like below with shifting the frame,

df['shift1'] = df.var1.shift(1)
df['shift2'] = df.var1.shift(2)
df['avg'] = df[['var1','shift1','shift2']].mean(axis=1)
df.iloc[range(2,n,3)]