creating a column which keeps a running count of consecutive values

Question

I am trying to create a column (“consec”) which will keep a running count of consecutive values in another (“binary”) without using loop. This is what the desired outcome would look like:

.    binary consec
1       0      0
2       1      1
3       1      2
4       1      3
5       1      4
5       0      0
6       1      1
7       1      2
8       0      0

However, this...

df['consec'][df['binary']==1] = df['consec'].shift(1) + df['binary']

results in this...

.  binary   consec
0     1       NaN
1     1       1
2     1       1
3     0       0
4     1       1
5     0       0
6     1       1
7     1       1
8     1       1
9     0       0

I see other posts which use grouping or sorting, but unfortunately, I don't see how that could work for me.

Answer 1

You can use the compare-cumsum-groupby pattern (which I really need to getting around to writing up for the documentation), with a final cumcount:

>>> df = pd.DataFrame({"binary": [0,1,1,1,0,0,1,1,0]})
>>> df["consec"] = df["binary"].groupby((df["binary"] == 0).cumsum()).cumcount()
>>> df
   binary  consec
0       0       0
1       1       1
2       1       2
3       1       3
4       0       0
5       0       0
6       1       1
7       1       2
8       0       0

---

This works because first we get the positions where we want to reset the counter:

>>> (df["binary"] == 0)
0     True
1    False
2    False
3    False
4     True
5     True
6    False
7    False
8     True
Name: binary, dtype: bool

The cumulative sum of these gives us a different id for each group:

>>> (df["binary"] == 0).cumsum()
0    1
1    1
2    1
3    1
4    2
5    3
6    3
7    3
8    4
Name: binary, dtype: int64

And then we can pass this to groupby and use cumcount to get an increasing index in each group.