Calculating max total over a period of time where values are increase and decrease

user_7323391 0
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I have a situation where I need to calculate the total number of clients for a day from a DataFrame where the values increase and decrease. But here is the catch:

If I have a Dataframe like so

DATETIME                CLIENTS
2018-03-03 08:00:00     1
2018-03-03 09:00:00     2
2018-03-03 10:00:00     3
2018-03-03 11:00:00     4
2018-03-03 12:00:00     5
2018-03-03 13:00:00     3
2018-03-03 14:00:00     4
2018-03-03 15:00:00     5

The max total number of clients for this day is 7 because it rises to 5 at 12:00:00 then the value decreases the next hour BUT we do not subtract from 5 and then it rises to 4 at 14:00:00 so we ADD 1 and 5 at 15:00:00 so we ADD another 1 so in total there are 7 max clients throughout the day.

I have tried cumsum() and MAX() as thought these would be useful but alas...

I need to implement this either in SQL or Python. Would appreciate any help!

pandas numpy mysql sql python
2 Answers

You logic is that you only want to count the coming-in visitors, not the leaving ones. Now, if you take diff(), then those coming-in are positive and leaving are negative. So we can just mask the negative with 0 and sum again.

Let's try:

dates = df.DATETIME.dt.normalize()

max_visitors = (df.groupby(dates)['CLIENTS'].diff()  # find the difference
                  .fillna(df['CLIENTS'])             # these are the first records in the day
                  .clip(0)                           # replace negatives with 0
                  .groupby(dates).sum()              # sum by days
               )

Output:

DATETIME
2018-03-03    7.0
Name: CLIENTS, dtype: float64
user_42384080

If your version of MySql is 8.0+ then you can use LAG() window function and aggregation:

select
  sum(case when clients > prev then clients - prev end) total
from (
  select *, lag(clients, 1, 0) over (order by datetime) prev
  from tablename
  where date(datetime) = '2018-03-03'
) t

See the demo.

user_104988280
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