Pandas-基于重叠时间段的拆分数据集

Question

I have reporting time periods that start on Mondays, end on Sundays, and run for 5 weeks. For example:

11/20/2017 - 12/24/2017 = t1 
11/27/2017 - 12/31/2017 = t2

I have a dataframe that consists of 6 of these periods (starting 11/20/2017) and I'm trying to split it into 6 dataframes for each time period using the LeaveDate column. My data looks like this:

Barcode LeaveDate  
ABC123  2017-11-22 
ABC124  2017-12-04  
ABC125  2017-12-15

As the dataframe is separated, some of the barcodes will fall into multiple periods- that's OK. I know I can do:

df['period'] = df['LeaveDate'].dt.to_period('M-SUN')
df['week'] = df['period'].dt.week

To get single weeks, but I don't know how to definte a "multi-week" period. The problem also is that a barcode can full under multiple periods, so they need to be outputted to multiple dataframes. Any ideas? Thanks!

Answer 1

There might be a more succinct solution, but this should work (will give you a dictionary of DataFrames, one for each period):

df = pd.DataFrame([['ABC123', '2017-11-22'],
                   ['ABC124',  '2017-12-04'],
                   ['ABC125',  '2017-12-15']],
                   columns=['Barcode', 'LeaveDate'])
periods = [('2017-11-20', '2017-12-24'), ('2017-11-27', '2017-12-31')]

results = {}
for period in periods:
    period_df = df[(df['LeaveDate'] >= period[0]) & (df['LeaveDate'] <= period[1])]
    results[period] = period_df