Group rows by certain timeperiod dending on other factors

Here is a way to do it with pd.merge_asof(). Let’s create our data:

data = {'id': [1,1,1,1,1,1,1],
        'datetime': ['2020-01-14 00:12:00', 
                    '2020-01-17 00:23:00',
                    '2020-01-17 00:13:00',
                    '2020-01-20 00:05:00',
                    '2020-03-10 00:07:00',
                    '2020-05-19 00:00:00',
                    '2020-05-20 00:00:00'],
        'ind': [0,1,0,0,0,0,1]
       }

df = pd.DataFrame(data)
df['datetime'] = df['datetime'].astype('datetime64')

Data:

   id            datetime  ind
0   1 2020-01-14 00:12:00    0
1   1 2020-01-17 00:23:00    1
2   1 2020-01-17 00:13:00    0
3   1 2020-01-20 00:05:00    0
4   1 2020-03-10 00:07:00    0
5   1 2020-05-19 00:00:00    0
6   1 2020-05-20 00:00:00    1

Next, let’s add a date to the dataset and pull all dates where the indicator is 1.

df['date'] = df['datetime'].dt.date.astype('datetime64')
df2 = df.loc[df['ind'] == 1, ['id', 'date', 'ind']].rename({'ind': 'ind2'}, axis=1)

Which gives us this:

df:

   id            datetime  ind       date
0   1 2020-01-14 00:12:00    0 2020-01-14
1   1 2020-01-17 00:23:00    1 2020-01-17
2   1 2020-01-17 00:13:00    0 2020-01-17
3   1 2020-01-20 00:05:00    0 2020-01-20
4   1 2020-03-10 00:07:00    0 2020-03-10
5   1 2020-05-19 00:00:00    0 2020-05-19
6   1 2020-05-20 00:00:00    1 2020-05-20

df2:

   id       date  ind2
1   1 2020-01-17     1
6   1 2020-05-20     1

Now let’s join them using pd.merge_asof() with direction=forward and a tolerance of 10 days. This will join all data up to 10 days looking forward.

df = pd.merge_asof(df.drop('ind', axis=1), df2, by='id', on='date', tolerance=pd.Timedelta('10d'), direction='forward')

Which gives us this:

   id            datetime  ind       date  ind2
0   1 2020-01-14 00:12:00    0 2020-01-14   1.0
1   1 2020-01-17 00:23:00    1 2020-01-17   1.0
2   1 2020-01-17 00:13:00    0 2020-01-17   1.0
3   1 2020-01-20 00:05:00    0 2020-01-20   NaN
4   1 2020-03-10 00:07:00    0 2020-03-10   NaN
5   1 2020-05-19 00:00:00    0 2020-05-19   1.0
6   1 2020-05-20 00:00:00    1 2020-05-20   1.0

Next, let’s work on creating groups. There are three rules we want to use:

1. The next value of ind2 is NaN
2. The next value of ID is not the current value of ID (we’re at the last value in the group)
3. The next day is 10 days greater than the current

With these rules, we can create a Boolean which we can then cumulatively sum to create our groups.

df['group_id'] = df['ind2'].eq(  (df['ind2'].shift() == np.NaN) 
                               | (df['id'].shift() != df['id'])
                               | (df['date'] - df['date'].shift() > pd.Timedelta('10d') ) 
                              ).cumsum()

    id            datetime  ind       date  ind2  group_id
0    1 2020-01-14 00:12:00    0 2020-01-14   1.0         1
1    1 2020-01-17 00:23:00    1 2020-01-17   1.0         1
2    1 2020-01-17 00:13:00    0 2020-01-17   1.0         1
3    1 2020-01-20 00:05:00    0 2020-01-20   NaN         1
4    1 2020-03-10 00:07:00    0 2020-03-10   NaN         1
5    1 2020-05-19 00:00:00    0 2020-05-19   1.0         2
6    1 2020-05-20 00:00:00    1 2020-05-20   1.0         2

Now we need to drop all the NaNs from ind2, remove date and we’re done.

df = df.dropna(subset='ind2').drop(['date', 'ind2'], axis=1)

Final output:

   id            datetime  ind  group_id
0   1 2020-01-14 00:12:00    0         1
1   1 2020-01-17 00:23:00    1         1
2   1 2020-01-17 00:13:00    0         1
5   1 2020-05-19 00:00:00    0         2
6   1 2020-05-20 00:00:00    1         2

One way to approach this problem is to create a new column called group that will hold the group identifier for each row. You can then iterate through the rows of the dataframe and assign a group identifier to each row based on its datetime and the datetime and indicator values of the other rows with the same id.

Here’s some code that demonstrates how this could be done:

import pandas as pd

# create a new column called 'group' with a default value of None
df['group'] = None

# iterate through the rows of the dataframe
for i, row in df.iterrows():
    # if the current row has an indicator value of 1
    if row['indicator'] == 1:
        # get the rows with the same id as the current row
        rows = df[df['id'] == row['id']]
        # find the datetime of the current row
        datetime = row['datetime']
        # find the rows with datetimes that are at most 10 days before the current row's datetime
        in_range = rows[rows['datetime'] >= datetime - pd.Timedelta(days=10)]
        # assign the group identifier 'A' to the rows in the range
        df.loc[in_range.index, 'group'] = 'A'

# drop the rows that don't have a group identifier
df = df[df['group'].notnull()]

This code will create a new column called group that contains a group identifier for each row. The rows with the same id and a datetime within 10 days of an indicator value of 1 will be assigned the group identifier ‘A’, and all other rows will be dropped.

You can modify this code to handle multiple indicator values for each id, by using a different group identifier for each indicator value and changing the group column to be a categorical column rather than a string column. You can also use the apply() function to apply a custom function to each group of rows with the same id, which can make the code more concise and easier to read.