[FIXED] Fast way to iterate and apply condition thought dataframe

Issue

I have a large dataframe such as below:

     vehicle id  delta
0            0      0
1            0     20
2            0     40
3            0    400
4            0     10
5            1      0
6            1     10
7            1    500
8            1     10
9            1     10
10           1    100
11           1     10

I want to add a new column as 'Trip' for each different vehicle that starts with trip_1 and if the delta is more than 50, then it adds a number to the trip number so the results would be as follow:

      vehicle id  delta   Trip
0            0      0     trip_1
1            0     20     trip_1
2            0     40     trip_1
3            0    400     trip_2
4            0     10     trip_2
5            1      0     trip_1
6            1     10     trip_1
7            1    500     trip_2
8            1     10     trip_2
9            1     10     trip_2
10           1    100     trip_3
11           1     10     trip_3

I'm thinking about using iterrow() but I want to avoid it since the dataframe is huge. Any suggestions?

Solution

Try this:

df['Trip'] = 'trip_' + df.groupby('id')['delta'].transform(
    lambda grp: np.where(grp > 50, 1, 0).cumsum()+1).apply(str)
print(df)

---

    vehicle  id  delta    Trip
0         0   0      0  trip_1
1         1   0     20  trip_1
2         2   0     40  trip_1
3         3   0    400  trip_2
4         4   0     10  trip_2
5         5   1      0  trip_1
6         6   1     10  trip_1
7         7   1    500  trip_2
8         8   1     10  trip_2
9         9   1     10  trip_2
10       10   1    100  trip_3
11       11   1     10  trip_3

Answered By - I'mahdi