Pandas dataframe filter rows on within time range

Question

I have a dataframe object like this:

 Date              ID           Delta
2019-10-16 16:43:46 BA9565P     0 days 00:00:00
2019-10-17 05:28:36 BA9565P     0 days 12:44:50
2019-10-16 16:43:13 BA9565X     0 days 00:00:00
2019-10-17 03:26:52 BA9565X     0 days 10:43:39
2019-10-10 19:17:17 BABRGNR     0 days 00:00:00
2019-10-12 19:43:56 BABRGNR     2 days 00:26:39
2019-10-31 00:48:52 BABRGR8     0 days 00:00:00
2019-11-01 14:33:41 BABRGR8     1 days 13:44:49

If the same ID are within 3 days of each other, then I only need the latest result. However if the same ID are more than 3 days apart, then I want to keep both records. So far I have done this.

df2 = df[df.duplicated(['ID'], keep = False)][['Date', 'ID']]
df2["Date"] = pd.to_datetime(df2["Date"])
df2["Delta"] = df2.groupby(['ID']).diff() 
df2["Delta"] = df2["Delta"].fillna(datetime.timedelta(seconds=0))

However I am not sure how should I continue. I have tried:

df2["Delta2"] = (df2["Delta"] < datetime.timedelta(days=3)

The condition would be True for the first element of the group whether they are within 3 days or not.

df2.groupby(['ID']).filter(lambda x: ((x["Delta"]<datetime.timedelta(days=3)) & \
                                             (x["Delta"] != datetime.timedelta(seconds=0))).any())

Again, it has a similar problem due to .diff() always return "NaT" for the first element. Is there a way to access the last element of the group? Or is there a better way than use groupby().diff() ?

jezrael · Accepted Answer · 2019-12-02 08:48:27Z

Solution select all rows of group if difference is more like 3 days per group else last rows for all another groups:

print (df)
                 Date       ID            Delta
0 2019-10-16 16:43:46  BA9565P  0 days 00:00:00
1 2019-10-17 05:28:36  BA9565P  0 days 12:44:50
2 2019-10-16 16:43:13  BA9565X  0 days 00:00:00
3 2019-10-20 03:26:52  BA9565X  0 days 10:43:39 <-chnaged data sample to 2019-10-20
4 2019-10-10 19:17:17  BABRGNR  0 days 00:00:00
5 2019-10-12 19:43:56  BABRGNR  2 days 00:26:39
6 2019-10-31 00:48:52  BABRGR8  0 days 00:00:00
7 2019-11-01 14:33:41  BABRGR8  1 days 13:44:49

#if not sorted dates
#df = df.sort_values(['ID','Date'])
df2 = df[df.duplicated(['ID'], keep = False)]
#get differences
df2["Delta"] = df2.groupby(['ID'])['Date'].diff().fillna(pd.Timedelta(0))
#compare by 3 days
mask = df2["Delta"] < pd.Timedelta(days=3)
#test if all Trues per groups
mask1 = mask.groupby(df2['ID']).transform('all')
#get last row per ID
mask2 = ~df2["ID"].duplicated(keep='last')

#filtering
df2 = df2[~mask1 | mask2]
print (df2)
                 Date       ID           Delta
1 2019-10-17 05:28:36  BA9565P 0 days 12:44:50
2 2019-10-16 16:43:13  BA9565X 0 days 00:00:00
3 2019-10-20 03:26:52  BA9565X 3 days 10:43:39
5 2019-10-12 19:43:56  BABRGNR 2 days 00:26:39
7 2019-11-01 14:33:41  BABRGR8 1 days 13:44:49

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