I have a df containing sub-trajectories (segments) of users, with mode of travel indicated by 0,1,2... which looks like this:
df = pd.read_csv('sample.csv')
df
id lat lon mode
0 5138001 41.144540 -8.562926 0
1 5138001 41.144538 -8.562917 0
2 5138001 41.143689 -8.563012 0
3 5138003 43.131562 -8.601273 1
4 5138003 43.132107 -8.598124 1
5 5145001 37.092095 -8.205070 0
6 5145001 37.092180 -8.204872 0
7 5145015 39.289341 -8.023454 2
8 5145015 39.197432 -8.532761 2
9 5145015 39.198361 -8.375641 2
In the above sample, id is for the segments but a full trajectory maybe covered by different modes (i.e. contains multiple segments).
So the first 4-digits of id is the unique trajectories, and the last 3-digits, unique segment with that trajectory.
I know that I can count the number of unique segments in the dfusing:
df.groupby('id').['mode'].nunique()
How do I then count the number of unique trajectories 5138, 5145, ...?
Use indexing for get first 4 values with str, if necessary first convert values to strings by Series.astype:
df = df.groupby(df['id'].astype(str).str[:4])['mode'].nunique().reset_index(name='count')
print (df)
id count
0 5138 2
1 5145 2
If need processing values after first 4 ids:
s = df['id'].astype(str)
df = s.str[4:].groupby(s.str[:4]).nunique().reset_index(name='count')
print (df)
id count
0 5138 2
1 5145 2
Another idea is use lambda function:
df.groupby(df['id'].apply(lambda x: str(x)[:4]))['mode'].nunique()