How to count values that meet a condition in a loop dataframe?

Question

I have a pandas df with a series in Distanz , and a series in Zielcode . I need to divide the Distanz by the number of intervals that repeat themselves. So the first interval different to zero would be divided by one , the second one by three and the third one by two .

Distanz    Zielcode
0.0         0
0.0         0
1.1         2
0.0         0
8.0         7
8.0         7
8.0         7
0.0         0
3.4         1
3.4         1
0.0         0

How to count the number of intervals within the whole series that meet this condition of repeat and the divide the value distanz by this count?

The desired output should look like this:

Distanz    Zielcode       Distanz - Output
0.0         0               0.0
0.0         0               0.0
1.1         2               1.1
0.0         0               0.0
8.0         7               2.7
8.0         7               2.7
8.0         7               2.7
0.0         0               0.0
3.4         1               1.7
3.4         1               1.7
0.0         0               0.0

Answer 1

I would split the problem in different steps.

1. Identify repeating elements:

    block = ((df['Distanz'].shift() != df['Distanz']) | (df['Zielcode'].shift() != df['Zielcode'])).cumsum()

   This gives:

    0 1 1 1 2 2 3 3 4 4 5 4 6 4 7 5 8 6 9 6 10 7 dtype: int32

2. Compute the size of each block:

    count = df.groupby(block).apply(lambda x: x.assign(count=len(x)) )['count'].reset_index(level=0, drop=True)

   This gives:

    0 2 1 2 2 1 3 1 4 3 5 3 6 3 7 1 8 2 9 2 10 1 Name: count, dtype: int64

3. Compute the new column:

    df['Distanz - Output'] = df['Distanz'] / count

And the dataframe becomes:

    Distanz  Zielcode  Distanz - Output
0       0.0         0          0.000000
1       0.0         0          0.000000
2       1.1         2          1.100000
3       0.0         0          0.000000
4       8.0         7          2.666667
5       8.0         7          2.666667
6       8.0         7          2.666667
7       0.0         0          0.000000
8       3.4         1          1.700000
9       3.4         1          1.700000
10      0.0         0          0.000000