分组并减去熊猫中的第一次出现和最后一次出现

Question

I have following dataframe in pandas

 code    date         time         dip     flag   tank   qty
 123     2018-12-23   08:00:00     389     0      1      1300
 123     2018-12-23   09:00:00     380     0      1      1250
 123     2018-12-23   10:00:00     378     0      1      1200
 123     2018-12-23   11:00:00     345     1      1      1150
 123     2018-12-23   12:00:00     342     1      1      1100
 123     2018-12-23   13:00:00     340     1      1      1050
 123     2018-12-23   14:00:00     338     1      1      1000
 123     2018-12-23   15:00:00     380     0      1      1500
 123     2018-12-23   16:00:00     340     1      1      1000
 123     2018-12-23   17:00:00     340     1      1      1000
 123     2018-12-23   08:00:00     389     0      2      1300
 123     2018-12-23   09:00:00     380     0      2      1250
 123     2018-12-23   10:00:00     378     0      2      1200
 123     2018-12-23   11:00:00     345     1      2      1150
 123     2018-12-23   12:00:00     342     1      2      1100
 123     2018-12-23   13:00:00     340     1      2      1050
 123     2018-12-23   14:00:00     338     1      2      1000

I want to find how many times dip is below 350, till what time(in hours) it remained below 350 and what is the quantity sold when below 350 Below is my desired dataframe. I have already set the flag as 1 when there is a dip less than 350

 code    date        tank     frequency    qty_sold    time
 123     2018-12-23  1        4            150         3
 123     2018-12-23  2        4            150         3

I am able to find the frequency with groupby. need some help in finding other two

  df_agg= df.groupby(['code','date','tank']).agg({'flag':['sum']}).reset_index()

Answer 1

Use:

#create datetimes column
df['datetime'] = pd.to_datetime(df['date'] + ' ' + df['time'])

#add aggregation by first and last 
df_agg= df[df['dip'] < 350].groupby(['code','date','tank']).agg({'flag':['sum'], 
                                                                'datetime':['first','last'],
                                                                'qty':['first','last']})
#flatten MultiIndex
df_agg.columns = df_agg.columns.map('_'.join)

#substract columns, timedeltas convert to hours
df_agg['qty_sold'] = df_agg.pop('qty_first') - df_agg.pop('qty_last') 
df_agg['time'] = (df_agg.pop('datetime_last') - df_agg.pop('datetime_first'))
                       .dt.total_seconds().div(3600).astype(int)
#rename column and create default index
df_agg = df_agg.rename(columns={'flag_size':'frequency'}).reset_index()

print (df_agg)
   code        date  tank  flag_sum  qty_sold  time
0   123  2018-12-23     1         4       150     3
1   123  2018-12-23     2         4       150     3

EDIT:

Solution working if no missing values in date or time values and frequency of datetimes is one hour difference.

Idea is create new helper column g for groups if difference is more like 1 hour and last aggregate sum per first 3 levels:

df['datetime'] = pd.to_datetime(df['date'] + ' ' + df['time'])

df_agg= df[df['dip'] < 350].copy()

df_agg['g'] = (df_agg.groupby(['code','date','tank'])['datetime'].diff()
                     .ne(pd.Timedelta(1, 'H'))
                     .cumsum())

df_agg= df_agg.groupby(['code','date','tank','g']).agg({'flag':['sum'], 
                                                        'datetime':['first','last'],
                                                        'qty':['first','last']})
df_agg.columns = df_agg.columns.map('_'.join)
df_agg['qty_sold'] = df_agg.pop('qty_first') - df_agg.pop('qty_last') 
df_agg['time'] = ((df_agg.pop('datetime_last') - df_agg.pop('datetime_first'))
                         .dt.total_seconds().div(3600).astype(int))

df_agg = (df_agg.rename(columns={'flag_size':'frequency'})
                .sum(level=[0,1,2])
                .reset_index()
          )

print (df_agg)
   code        date  tank  flag_sum  qty_sold  time
0   123  2018-12-23     1         6       150     4
1   123  2018-12-23     2         4       150     3

Answer 2

You can do:

# to get till what time (hour)
df.loc[df['dip'].lt(350),'time'].dt.hour.max()

# what is the quantity sold
df.loc[df['dip'].lt(350),'qty'].sum()