Faster way to count number of timestamps before another timestamp - python-3.x

Question

I have two dataframe "train" and "log". "log" has datetime columns "time1" while train has datetime column "time2". For every row in "train" I want to find out counts of "time1" when "time1" is before "time2".
I already tried the apply method with dataframe.
def log_count(row):
return sum((log['user_id'] == row['user_id']) & (log['time1'] < row['time2']))
train.apply(log_count, axis = 1)
It is taking very long with this approach.

Answer 1

Since you want to do this once for each (paired) user_id group, you could do the following:
Create a column called is_log which is 1 in log and 0 in train:
log['is_log'] = 1
train['is_log'] = 0
The is_log column will be used to keep track of whether or not a row comes from log or train.
Concatenate the log and train DataFrames:
combined = pd.concat(
[log.rename(columns=dict(time1="time")), train.rename(columns=dict(time2="time"))],
axis=0,
ignore_index=True,
sort=False,
)
Sort the combined DataFrame by user_id and time:
combined = combined.sort_values(by=["user_id", "time"])
So now combined looks something like this:
time user_id is_log
6 2000-01-17 0 0
0 2000-03-13 0 1
1 2000-06-08 0 1
7 2000-06-25 0 0
4 2000-07-09 0 1
8 2000-07-18 0 0
10 2000-03-13 1 0
5 2000-04-16 1 0
3 2000-08-04 1 1
9 2000-08-17 1 0
2 2000-10-20 1 1
Now the count that you are looking for can be expressed as a cumulative sum of the is_log column, grouped by user_id:
combined["count"] = combined.groupby("user_id")["is_log"].cumsum()
train = combined.loc[combined["is_log"] == 0]
This is the main idea: Counting the number of 1s in the is_log column is equivalent to counting the number of times in log which come before each time in train.
For example,
import numpy as np
import pandas as pd
np.random.seed(2019)
def random_dates(N):
return np.datetime64("2000-01-01") + np.random.randint(
365, size=N
) * np.timedelta64(1, "D")
N = 5
log = pd.DataFrame({"time1": random_dates(N), "user_id": np.random.randint(2, size=N)})
train = pd.DataFrame(
{
"time2": np.r_[random_dates(N), log.loc[0, "time1"]],
"user_id": np.random.randint(2, size=N + 1),
}
)
log["is_log"] = 1
train["is_log"] = 0
combined = pd.concat(
[log.rename(columns=dict(time1="time")), train.rename(columns=dict(time2="time"))],
axis=0,
ignore_index=True,
sort=False,
)
combined = combined.sort_values(by=["user_id", "time"])
combined["count"] = combined.groupby("user_id")["is_log"].cumsum()
train = combined.loc[combined["is_log"] == 0]
print(log)
# time1 user_id is_log
# 0 2000-03-13 0 1
# 1 2000-06-08 0 1
# 2 2000-10-20 1 1
# 3 2000-08-04 1 1
# 4 2000-07-09 0 1
print(train)
yields
time user_id is_log count
6 2000-01-17 0 0 0
7 2000-06-25 0 0 2
8 2000-07-18 0 0 3
10 2000-03-13 1 0 0
5 2000-04-16 1 0 0
9 2000-08-17 1 0 1