I have a pandas dataframe as below:
| id | location | timestamp |
|---|---|---|
| 001 | A | 2022-01-01 17:00:00 |
| 001 | B | 2022-01-01 18:00:00 |
| 001 | B | 2022-01-01 18:15:00 |
| 002 | B | 2022-01-01 18:30:00 |
| 003 | B | 2022-01-01 19:00:00 |
| 003 | A | 2022-01-01 20:00:00 |
I'd like to filter records where location is B and only after the same id has visited location A (i.e. the timestamp of B is later than the timestamp of A). Desired output as below:
| id | location | timestamp |
|---|---|---|
| 001 | B | 2022-01-01 18:00:00 |
| 001 | B | 2022-01-01 18:15:00 |
Thank you for your help!
CodePudding user response：
You can use boolean indexing with help of groupby.cummax:
# identify all times of a visit to A and after
m1 = df.sort_values('timestamp')['location'].eq('A').groupby(df['id']).cummax()
# identify location B
m2 = df['location'].eq('B')
# keep rows where both conditions above are True
df[m1&m2]
output:
id location timestamp
1 1 B 2022-01-01 18:00:00
2 1 B 2022-01-01 18:15:00
CodePudding user response：
You can simply compare the dates via python comparators as shown here:
filtered_records = records[records["timestamp"] > compared_record["timestamp"]]
The Location can be filteres in the same way:
filtered_records = records[records["location"] == compared_record["location"]]
So your final code would be:
filtered_records = records[(records["timestamp"] > compared_record["timestamp"]) && (records["location"] == compared_record["location"])]