pandas: add week dates with dataframe

Question

I have a df like, which has such rows:

    p_id    m_id    x_id    g_id    u_id
0   2       NaN     1408    7       121
1   3       1259    117     23      315
2   3       1259    221     9       718
3   3       1259    397     76      367

and two datetime objects:

start_date:

datetime.datetime(2021, 5, 25, 0, 0)

end_date:

datetime.datetime(2021, 5, 29, 0, 0)

how do I get a df like, basically (adding the week-dates from start_date to end_date with each row):

    p_id    m_id    x_id    g_id    u_id  s_date
0   2       NaN     1408    7       121   2021-05-25
1   2       NaN     1408    7       121   2021-05-26
2   2       NaN     1408    7       121   2021-05-27
3   2       NaN     1408    7       121   2021-05-28
4   2       NaN     1408    7       121   2021-05-29

5   3       1259    117     23      315   2021-05-25
6   3       1259    117     23      315   2021-05-26
7   3       1259    117     23      315   2021-05-27
8   3       1259    117     23      315   2021-05-28
9   3       1259    117     23      315   2021-05-29
.
.
15  3       1259    397     76      367   2021-05-25
16  3       1259    397     76      367   2021-05-26
17  3       1259    397     76      367   2021-05-27
18  3       1259    397     76      367   2021-05-28
19  3       1259    397     76      367   2021-05-29

Answer 1

Generate date_range and cross merge

1. In pandas version >= 1.2x, to perform a cross merge we can now pass an optional parameter how='cross' to the merge function

dates = pd.date_range(start_date, end_date)
df.merge(dates.to_series(name='s_date'), how='cross')

2. For pandas version < 1.2x we have to create a temporary merge key in order to perform the cross merge

dates = pd.date_range(start_date, end_date)
df.assign(k=1).merge(dates.to_frame(name='s_date').assign(k=1), on='k').drop('k', 1)

---

    p_id    m_id  x_id  g_id  u_id     s_date
0      2     NaN  1408     7   121 2021-05-25
1      2     NaN  1408     7   121 2021-05-26
2      2     NaN  1408     7   121 2021-05-27
3      2     NaN  1408     7   121 2021-05-28
4      2     NaN  1408     7   121 2021-05-29
5      3  1259.0   117    23   315 2021-05-25
6      3  1259.0   117    23   315 2021-05-26
7      3  1259.0   117    23   315 2021-05-27
8      3  1259.0   117    23   315 2021-05-28
9      3  1259.0   117    23   315 2021-05-29
10     3  1259.0   221     9   718 2021-05-25
11     3  1259.0   221     9   718 2021-05-26
12     3  1259.0   221     9   718 2021-05-27
13     3  1259.0   221     9   718 2021-05-28
14     3  1259.0   221     9   718 2021-05-29
15     3  1259.0   397    76   367 2021-05-25
16     3  1259.0   397    76   367 2021-05-26
17     3  1259.0   397    76   367 2021-05-27
18     3  1259.0   397    76   367 2021-05-28
19     3  1259.0   397    76   367 2021-05-29