My data contained the time when the field force visited a client. What I need to do, is to compute for each day and for each client the occurrences of the visits (in between specific time ranges - for example, every 15minutes from 8am to 8pm.) Ideally, to draw the distribution of an histogram with the time interval on the x-axis and the occurrences on the y-axis.
This how my current data frame looks like:
Client | Hour | Day |
---|---|---|
A | 11:14:48 | Monday |
A | 11:24:34 | Monday |
B | 15:34:34 | Tuesday |
B | 13:34:35 | Tuesday |
B | 15:10:22 | Tuesday |
B | 15:01:02 | Tuesday |
... | ... | ... |
The output should be something like this, than I can use to plot an histogram:
Interval | Client | Occurrences | Day |
---|---|---|---|
8:00:00 - 8:15:00 | A | 0 | Monday |
... | ... | ... | ... |
11:00:00 - 11:15:00 | A | 1 | Monday |
11:15:00 - 11:30:00 | A | 1 | Monday |
... | ... | ... | ... |
Thanks in advance!
CodePudding user response:
Admittedly hacky, but should work. If anyone has a better solution, please let me know. This would be way easier if you had actual date-times instead of a mix between a time interval and day names.
Here is the data I am using:
df = pd.DataFrame({'Client':['A', 'A', 'B', 'B', 'B', 'B'],
'Hour': ['11:14:48', '11:24:34', '15:34:34', '13:34:35', '15:10:22', '15:01:02'],
'Day':['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Tuesday', 'Tuesday']})
Here the code:
TIME_START = '08:00:00'
TIME_END = '20:00:00'
INTERVAL = '15min'
def reindex_by_date(df):
df['Hour'] = pd.to_datetime('1970-1-1 ' df['Hour'].astype(str))
dt_index = pd.DatetimeIndex(pd.date_range(start=f'1970-1-1 {TIME_START}', end=f'1970-1-1 {TIME_END}', freq=INTERVAL))
resampled_df = df.resample('15min', on='Hour').count().reindex(dt_index).fillna(0).rename(columns={'Hour':'Occurrences'}).rename_axis('Hour').reset_index()
resampled_df['Client'] = df['Client'].iat[0]
resampled_df['Day'] = df['Day'].iat[0]
resampled_df['Hour'] = resampled_df['Hour'].dt.strftime('%H:%M:%S') ' - ' (resampled_df['Hour'] pd.Timedelta(minutes=15)).dt.strftime('%H:%M:%S')
return resampled_df.rename(columns={'Hour':'Interval'})
result = df.groupby(['Client', 'Day'], as_index=False).apply(reindex_by_date).reset_index(0, drop=True)
result
looks like this:
Interval Client Occurrences Day
0 08:00:00 - 08:15:00 A 0.0 Monday
1 08:15:00 - 08:30:00 A 0.0 Monday
2 08:30:00 - 08:45:00 A 0.0 Monday
3 08:45:00 - 09:00:00 A 0.0 Monday
4 09:00:00 - 09:15:00 A 0.0 Monday
.. ... ... ... ...
44 19:00:00 - 19:15:00 B 0.0 Tuesday
45 19:15:00 - 19:30:00 B 0.0 Tuesday
46 19:30:00 - 19:45:00 B 0.0 Tuesday
47 19:45:00 - 20:00:00 B 0.0 Tuesday
48 20:00:00 - 20:15:00 B 0.0 Tuesday
[98 rows x 4 columns]
While the nonzero entries are:
Interval Client Occurrences Day
12 11:00:00 - 11:15:00 A 1.0 Monday
13 11:15:00 - 11:30:00 A 1.0 Monday
22 13:30:00 - 13:45:00 B 1.0 Tuesday
28 15:00:00 - 15:15:00 B 2.0 Tuesday
30 15:30:00 - 15:45:00 B 1.0 Tuesday