I have a large list of events being tracked with a timestamp appended to each:
I currently have the following table:
ID Time_Stamp Event
1 2/20/2019 18:21 0
1 2/20/2019 19:46 0
1 2/21/2019 18:35 0
1 2/22/2019 11:39 1
1 2/22/2019 16:46 0
1 2/23/2019 7:40 0
2 6/5/2019 0:10 0
3 7/31/2019 10:18 0
3 8/23/2019 16:33 0
4 6/26/2019 20:49 0
What I want is the following [but not sure if it's possible]:
ID Time_Stamp Conversion Total_Duration_Days Conversion_Duration
1 2/20/2019 18:21 0 2.555 1.721
1 2/20/2019 19:46 0 2.555 1.721
1 2/21/2019 18:35 0 2.555 1.721
1 2/22/2019 11:39 1 2.555 1.721
1 2/22/2019 16:46 1 2.555 1.934
1 2/23/2019 7:40 0 2.555 1.934
2 6/5/2019 0:10 0 1.00 0.000
3 7/31/2019 10:18 0 23.260 0.000
3 8/23/2019 16:33 0 23.260 0.000
4 6/26/2019 20:49 0 1.00 0.000
For #1 Total Duration = Max Date - Min Date [2.555 Days]
For #2 Conversion Duration = Conversion Date - Min Date [1.721 Days] - following actions post the conversion can remain at the calculated duration
I have attempted the following:
df.reset_index(inplace=True)
df.groupby(['ID'])['Time_Stamp].diff().fillna(0)
This kind of does what I want, but it's showing the difference between each event, not the min time stamp to the max time stamp
conv_test = df.reset_index(inplace=True)
min_df = conv_test.groupby(['ID'])['visitStartTime_aest'].agg('min').to_frame('MinTime')
max_df = conv_test.groupby(['ID'])['visitStartTime_aest'].agg('max').to_frame('MaxTime')
conv_test = conv_test.set_index('ID').merge(min_df, left_index=True, right_index=True)
conv_test = conv_test.merge(max_df, left_index=True, right_index=True)
conv_test['Durartion'] = conv_test['MaxTime'] - conv_test['MinTime']
This gives me Total_Duration_Days which is great [feel free to offer a more elegant solution
Any ideas on how I can get Conversion_Duration?
You can use GroupBy.transform with min and max for Series with same size like original, so possible subtract for Total_Duration_Days and then filter only 1 rows by Event, create Series by DataFrame.set_index and convert to dict, then Series.map for new Series, so possible subtract minimal values per groups:
df['Time_Stamp'] = pd.to_datetime(df['Time_Stamp'])
min1 = df.groupby('ID')['Time_Stamp'].transform('min')
max1 = df.groupby('ID')['Time_Stamp'].transform('max')
df['Total_Duration_Days'] = max1.sub(min1).dt.total_seconds() / (3600 * 24)
d = df.loc[df['Event'] == 1].set_index('ID')['Time_Stamp'].to_dict()
new1 = df['ID'].map(d)
Because possible multiple 1 per groups is added solution only for this groups - testing, if more 1 per groups in mask, get Series new2 and then use Series.combine_first with mapped Series new1.
Reason is improve performance, because a bit complicated processing multiple 1.
mask = df['Event'].eq(1).groupby(df['ID']).transform('sum').gt(1)
g = df[mask].groupby('ID')['Event'].cumsum().replace({0:np.nan})
new2 = (df[mask].groupby(['ID', g])['Time_Stamp']
.transform('first')
.groupby(df['ID'])
.bfill())
df['Conversion_Duration'] = (new2.combine_first(new1)
.sub(min1)
.dt.total_seconds().fillna(0) / (3600 * 24))
print (df)
ID Time_Stamp Event Total_Duration_Days Conversion_Duration
0 1 2019-02-20 18:21:00 0 2.554861 1.720833
1 1 2019-02-20 19:46:00 0 2.554861 1.720833
2 1 2019-02-21 18:35:00 0 2.554861 1.720833
3 1 2019-02-22 11:39:00 1 2.554861 1.720833
4 1 2019-02-22 16:46:00 1 2.554861 1.934028
5 1 2019-02-23 07:40:00 0 2.554861 1.934028
6 2 2019-06-05 00:10:00 0 0.000000 0.000000
7 3 2019-07-31 10:18:00 0 23.260417 0.000000
8 3 2019-08-23 16:33:00 0 23.260417 0.000000
9 4 2019-06-26 20:49:00 0 0.000000 0.000000