Increment Count column by 1 base on a another column - python-3.x

Question

I have this data frame where i need to create a count column base on my distance column. I grouped the result by the model column. What i anticipate to get is an increment by 1 on the next count row each time the distance is 100. For example, here is what I have so far but no success yet with the increment
import pandas as pd
df = pd.DataFrame(
[['A', '34', 3], ['A', '55', 5], ['A', '100', 7], ['A', '0', 1],['A', '55', 5],
['B', '90', 3], ['B', '0', 1], ['B', '1', 3], ['B', '21', 1],['B', '0', 1],
['C', '9', 7], ['C', '100', 4], ['C', '50', 1], ['C', '100', 6],['C', '22', 4]],
columns=['Model', 'Distance', 'v1'])
df = df.groupby(['Model']).apply(lambda row: callback(row) if row['Distance'] is not None else callback(row)+1)
print(df)
import numpy as np
(
df.groupby('Model')
.apply(lambda x: x.assign(Bount=x.Count + x.Distance.shift()
.eq('100').replace(False, np.nan)
.ffill().fillna(0)
)
)
.reset_index(level=0, drop=True)
)
Result with your code solution
Model Distance v1 Count
A 34 3 1.0
A 55 5 2.0
A 100 7 3.0
A 0 1 5.0
A 55 5 6.0
B 90 3 1.0
B 0 1 2.0
B 1 3 3.0
B 21 1 4.0
B 0 1 5.0
C 9 7 1.0
C 100 4 2.0
C 50 1 4.0
C 100 6 5.0
C 22 4 6.0
My expected result is:
Model Distance v1 Count
A 34 3 1.0
A 55 5 2.0
A 100 7 3.0
A 0 1 5.0
A 55 5 6.0
B 90 3 1.0
B 0 1 2.0
B 1 3 3.0
B 21 1 4.0
B 0 1 5.0
C 9 7 1.0
C 100 4 2.0
C 50 1 4.0
C 100 6 5.0
C 22 4 7.0
Take a look at the C group that are two distance equal to 100

Answer 1

Setup:
df = pd.DataFrame({'Model': {0: 'A', 1: 'A', 2: 'A', 3: 'A', 4: 'A', 5: 'B', 6: 'B', 7: 'B', 8: 'B', 9: 'B', 10: 'C', 11: 'C', 12: 'C', 13: 'C', 14: 'C'},
'Distance': {0: '34', 1: '55', 2: '100', 3: '0', 4: '55', 5: '90', 6: '0', 7: '1', 8: '21', 9: '0', 10: '9', 11: '23', 12: '100', 13: '33', 14: '23'},
'v1': {0: 3, 1: 5, 2: 7, 3: 1, 4: 5, 5: 3, 6: 1, 7: 3, 8: 1, 9: 1, 10: 7, 11: 4, 12: 1, 13: 6, 14: 4},
'Count': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 1, 6: 2, 7: 3, 8: 4, 9: 5, 10: 1, 11: 2, 12: 3, 13: 4, 14: 5}})
If the logic needs to be applied across the Model column, you can use a shift, compare and add 1 for eligibal rows:
df.loc[df.Distance.shift().eq('100'), 'Count'] += 1
If the logic needs to be applied at per Model group, then you can use a groupby:
(
df.groupby('Model')
.apply(lambda x: x.assign(Count=x.Distance.shift().eq('100') + x.Count))
.reset_index(level=0, drop=True)
)
Based on #StringZ's updates, below is the updated solution:
(
df.groupby('Model')
.apply(lambda x: x.assign(Count=x.Count + x.Distance.shift()
.eq('100').replace(False, np.nan)
.ffill().fillna(0)
)
)
.reset_index(level=0, drop=True)
)
Model Distance v1 Count
0 A 34 3 1.0
1 A 55 5 2.0
2 A 100 7 3.0
3 A 0 1 5.0
4 A 55 5 6.0
5 B 90 3 1.0
6 B 0 1 2.0
7 B 1 v3 3.0
8 B 21 1 4.0
9 B 0 1 5.0
10 C 9 7 1.0
11 C 23 4 2.0
12 C 100 1 3.0
13 C 33 6 5.0
14 C 23 4 6.0