Selecting data from one dataframe base on column in second dataframe - pandas

Question

I have a dataframe (df), contains datetime columns startdate, enddate and volume of product
If I want to look at one particular date that fit in between startdate and enddate and its total volume, i can do it with no problem at all (see code).
However if I create a second dataframe (call it report), create a list of date that I would like to look at the total volume of product from first df, I came up with an error:
Can only compare identically-labeled Series objects
I read up on things like dropping index on the second df or sorting dates but they don't seem to work
So my working code for requesting volume fitted within startdate and enddate, say first of july 2019:
df[(df['StartDate'] >= '2019-07-01') & (df['EndDate'] <= '2019-10-31')]['Volume'].sum()
but if i create a second df (report):
report = pd.Series(pd.date_range('today', periods=len(df), freq='D').normalize(),name='Date')
report = pd.DataFrame(report)
and request what i want to see:
report['trial'] = df[(df['StartDate'] >= report.Date) & (df['EndDate'] <= report.Date)]['Volume'].sum()
got this error: 'Can only compare identically-labeled Series objects'
Any advice/suggestions welcome, thanks!

Answer 1

First, some sample data:
np.random.seed(42)
dates = pd.date_range('2019-01-01', '2019-12-01', freq='MS')
df = pd.DataFrame({
'StartDate': dates,
'EndDate': dates + pd.offsets.MonthEnd(),
'Volume': np.random.randint(1, 10, len(dates))
})
StartDate EndDate Volume
0 2019-01-01 2019-01-31 7
1 2019-02-01 2019-02-28 4
2 2019-03-01 2019-03-31 8
3 2019-04-01 2019-04-30 5
4 2019-05-01 2019-05-31 7
5 2019-06-01 2019-06-30 3
6 2019-07-01 2019-07-31 7
7 2019-08-01 2019-08-31 8
8 2019-09-01 2019-09-30 5
9 2019-10-01 2019-10-31 4
10 2019-11-01 2019-11-30 8
11 2019-12-01 2019-12-31 8
And the report dates:
reports = pd.to_datetime(['2019-01-15', '2019-02-15', '2019-08-15'])
Using numpy's array broadcasting:
start = df['StartDate'].values
end = df['EndDate'].values
d = reports.values[:, None]
df[np.any((start <= d) & (d <= end), axis=0)]
Result:
StartDate EndDate Volume
0 2019-01-01 2019-01-31 7
1 2019-02-01 2019-02-28 4
7 2019-08-01 2019-08-31 8