insert rows with next 10 business days to dataframe - python-3.x

Question

df has three columns - date, name, and qty. For each name and date combination I want to insert n rows such that name is repeated in these next n rows but date is increased by 1 business day and qty=nan if that name and date combination doesn't exist already in df.
>>> import pandas as pd
>>> from datetime import datetime
>>> df = pd.DataFrame({'name':['abd']*3 + ['pqr']*2 + ['xyz']*1, 'date':[datetime(2020,1,6), datetime(2020,1,8), datetime(2020,2,5), datetime(2017,10,4), datetime(2017,10,13), datetime(2013,5,27)], 'qty':range(6)})
>>> df
name date qty
0 abd 2020-01-06 10
1 abd 2020-01-08 1
2 abd 2020-02-05 2
3 pqr 2017-10-04 3
4 pqr 2017-10-13 4
5 xyz 2013-05-27 5
I am not sure how to go about it. Any thoughts/clues. Thanks a lot!
Desired output for n=3:
name date qty
0 abd 2020-01-06 10
1 abd 2020-01-07 nan
2 abd 2020-01-08 1
3 abd 2020-01-09 nan
4 abd 2020-01-10 nan
5 abd 2020-01-13 nan
6 abd 2020-02-05 2
7 abd 2020-02-08 nan
8 abd 2020-02-09 nan
9 abd 2020-02-10 nan
10 pqr 2017-10-04 3
11 pqr 2017-10-05 nan
12 pqr 2017-10-06 nan
13 pqr 2017-10-09 nan
14 pqr 2017-10-13 4
15 pqr 2017-10-16 nan
16 pqr 2017-10-17 nan
17 pqr 2017-10-18 nan
18 xyz 2013-05-27 5
19 xyz 2013-05-28 nan
20 xyz 2013-05-29 nan
21 xyz 2013-05-30 nan

Answer 1

Here is a way:
from functools import reduce
n = 3
new_index = (
df.groupby("name")
.apply(
lambda x: reduce(
lambda i, j: i.union(j),
[pd.bdate_range(i, periods=n + 1) for i in x["date"]],
)
)
.explode()
)
midx = pd.MultiIndex.from_frame(new_index.reset_index(), names=["name", "date"])
df_out = df.set_index(["name", "date"]).reindex(midx).reset_index()
df_out
If explode cannot be used:
from functools import reduce
n = 3
new_index = (
df.groupby("name")
.apply(
lambda x: reduce(
lambda i, j: i.union(j),
[pd.bdate_range(i, periods=n + 1) for i in x["date"]],
)
)
.apply(pd.Series)
.stack()
.reset_index(level=0)
.rename(columns={0:'date'})
)
df_out = new_index.merge(df, how='left', on=['name', 'date'])
df_out
Output:
name date qty
0 abd 2020-01-06 0.0
1 abd 2020-01-07 NaN
2 abd 2020-01-08 1.0
3 abd 2020-01-09 NaN
4 abd 2020-01-10 NaN
5 abd 2020-01-13 NaN
6 abd 2020-02-05 2.0
7 abd 2020-02-06 NaN
8 abd 2020-02-07 NaN
9 abd 2020-02-10 NaN
10 pqr 2017-10-04 3.0
11 pqr 2017-10-05 NaN
12 pqr 2017-10-06 NaN
13 pqr 2017-10-09 NaN
14 pqr 2017-10-13 4.0
15 pqr 2017-10-16 NaN
16 pqr 2017-10-17 NaN
17 pqr 2017-10-18 NaN
18 xyz 2013-05-27 5.0
19 xyz 2013-05-28 NaN
20 xyz 2013-05-29 NaN
21 xyz 2013-05-30 NaN
How it works:
First import reduce from functools to use pd.Index.union to create a single list of dates. The list of dates is created from pd.bdate_range, with in groupby for each name. Convert that list of new_index, and names to a MultiIndex using pd.MultiIndex.from_frame. Use reindex after set_index on the original dataframe.