log-count-utils
Introduction & Usage
Suppose we have an action log data df like
| user_id | timestamp | expense |
|---|---|---|
| 0 | 2021-02-18 10:00:00 | 100 |
| 0 | 2021-02-18 10:00:10 | 10 |
| 0 | 2021-02-18 10:00:21 | 1 |
| 0 | 2021-02-18 11:00:21 | 0.1 |
| 1 | 2020-02-18 10:00:10 | 100 |
| 1 | 2020-02-18 10:00:20 | 10 |
| 1 | 2020-02-18 10:00:20 | 1 |
| 1 | 2020-02-18 10:00:29 | 0 |
Suppose that you have to compute the following quantity for each row in this dataframe:
- the number of actions each user has taken within 10 seconds
- total amount of expenses of a user within 10 seconds
The following naive way is fine for this tiny example but becomes costly (O(N^2)) for large data frame.
from datetime import timedelta
import numpy as np
td = timedelta(seconds=10)
answers = []
for uid, time_point in zip(df.user_id, df.timestamp):
cnt = np.sum(
(df.user_id == uid) & (df.timestamp < time_point) & (df.timestamp >= (time_point - td))
)
answers.append(cnt)
If df is sorted (by user_id as the primary and timestamp as the secondary key),
we can do this blazing fast (O(N)) using log_count_util.
from log_count_util import find_n_records_within_interval
answers = find_n_records_within_interval(
df.user_id, df.timestamp, df_user_id, df.timestamp, td
)
1 maintainer
Metadata
1 maintainer
Release files for log-count-util 0.1.0
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