if first value is zero in one dataframe set previous values to 1 in another dataframe on condition

i have 2 dataframes, df1 and df2 i want to change the values of df2 based on a condition from df1

df1

  name       date  flag
0  abc  4/11/2023     1
1  xyz   2/8/2023     0

df2:

  name       date  flag
0  xyz   2/6/2023     0
1  xyz   2/7/2023     0
2  xyz   2/8/2023     0
3  xyz   2/9/2023     1
4  xyz  2/10/2023     1
5  xyz  2/11/2023     1
6  xyz  2/12/2023     1
7  xyz  2/13/2023     1

in df1 for 'xyz', the flag is 0 on 2/8/2023 hence in df2 dates less than the date in df1 should be 1

expected output

I am new to python and want to do it using pandas functions

The exact logic is unclear, but you need to use a merge_asof to determine if there is a match per name with an later date:

# ensure datetime
df1['date'] = pd.to_datetime(df1['date'], dayfirst=False)
df2['date'] = pd.to_datetime(df2['date'], dayfirst=False)

out = (pd.merge_asof(df2.reset_index().sort_values(by='date'),
                     df1.sort_values(by='date'),
                     by='name', on='date', direction='forward',
                     allow_exact_matches=False
                     )
         .set_index('index').reindex(df2.index)
         .assign(flag=lambda d: d.pop('flag_x').mask(d.pop('flag_y').notna(), 1))
      )

Output:

      name       date  flag
index                      
0      xyz 2023-02-06     1
1      xyz 2023-02-07     1
2      xyz 2023-02-08     0
3      xyz 2023-02-09     1
4      xyz 2023-02-10     1
5      xyz 2023-02-11     1
6      xyz 2023-02-12     1
7      xyz 2023-02-13     1

Intermediate before the assign:

      name       date  flag_x  flag_y
index                                
0      xyz 2023-02-06       0     0.0
1      xyz 2023-02-07       0     0.0
2      xyz 2023-02-08       0     NaN
3      xyz 2023-02-09       1     NaN
4      xyz 2023-02-10       1     NaN
5      xyz 2023-02-11       1     NaN
6      xyz 2023-02-12       1     NaN
7      xyz 2023-02-13       1     NaN

Note that you can use a more complex logic if needed, the value in "flag_y" is the value of the matching date (here of 2023-02-08 for indices 0 and 2).

only one date per name in df1, or only considering the max date

If df1 only has one date per name, then you can simplify to:

df1['date'] = pd.to_datetime(df1['date'], dayfirst=False)
df2['date'] = pd.to_datetime(df2['date'], dayfirst=False)

m = df2['date'].lt(df2['name'].map(df1.set_index('name')['date']))
df.loc[m, 'flag'] = 1

Or, if several dates and you only want consider the max date per name:

m = df2['date'].lt(df2['name'].map(df1.groupby('name')['date'].max()))
df.loc[m, 'flag'] = 1