How to write to txt from python wherein date from header is copied to every record in txt file

I have a text file which looks like below (snapshot only):

DC000D20221110012022100019
DC011D           AV0019000300080180003340501031800481200000
DC011D           AV0019000300083180003361901031900071900000
DC011D           AV0019000300089180003378701032100515800000
DC000D20221209012022100019
DC011D           CG0019000300080220100264401000000000000000
DC011D           CG0019000300080220400885101000039990700000
DC011D           CG0019000300080220400885101000040013000000

I have ~1m records this way for 3 years. The First record 'DC000D20221110012022100019' is the header for which has date at position [6:14]. I need to import this data into a dataframe for my exploratory analysis for which I need the date present for each record, not header. So I need something like this:

DC000D20221110012022100019
DC011D20221110   AV0019000300080180003340501031800481200000
DC011D20221110   AV0019000300083180003361901031900071900000
DC011D20221110   AV0019000300089180003378701032100515800000
DC000D20221209012022100019
DC011D20221209   CG0019000300080220100264401000000000000000
DC011D20221209   CG0019000300080220400885101000039990700000
DC011D20221209   CG0019000300080220400885101000040013000000

That way I can import it into pandas df easily.

Form the main text file as above I am preparing another txt file with only DC011 records as below:

#File for submodule DC011

File1=open(r"\path\CRGDEC\CRGDEC.txt")
File2=open(r"\path\CRGDEC_DC011.txt", "w")

for line in File1.readlines():
    if (line.startswith('DC011')):
        File2.write(line)

But this eliminates the DC000 header records and I can't use the fillna() option in my df to prepare the Date column.
Help is much appreciated!!

N.B. I have other modules in a similar way as well (such as DC012, DC013, DC014).

If you just add an if to check for the header record, you can also write it (and memorize the date to append to the subsequent lines)

File1=open(r"\path\CRGDEC\CRGDEC.txt")
File2=open(r"\path\CRGDEC_DC011.txt", "w")

saveDate = ""   # just in case
for line in File1.readlines():
    if (line.startswith('DC000'):
        saveDate = line[6:14]
        File2.write(line)
    if (line.startswith('DC011')):
       line2write = line[:6] + saveDate + line[7:]
        File2.write(line2write)

I didn't test this, but it should be near correct.

You can import in pandas directly by specifying the separator and the column names in read_csv. Then proceed with some operations to get the desired format:

import pandas as pd
from io import StringIO

data = StringIO("""DC000D20221110012022100019
DC011D           AV0019000300080180003340501031800481200000
DC011D           AV0019000300083180003361901031900071900000
DC011D           AV0019000300089180003378701032100515800000
DC000D20221209012022100019
DC011D           CG0019000300080220100264401000000000000000
DC011D           CG0019000300080220400885101000039990700000
DC011D           CG0019000300080220400885101000040013000000
""")

df =  pd.read_csv(data, sep=r"\s+", engine="python", names=['ref', 'value'])
df['date'] = df.loc[df['value'].isna(), 'ref'].str[6:14]
df['date'] = df['date'].ffill()
mask = ~df['value'].isna()
df.loc[mask, 'ref'] = df[mask]['ref'] + df[mask]['date']
print(df.drop(columns='date'))

Output:

                          ref                                       value
0  DC000D20221110012022100019                                        None
1              DC011D20221110  AV0019000300080180003340501031800481200000
2              DC011D20221110  AV0019000300083180003361901031900071900000
3              DC011D20221110  AV0019000300089180003378701032100515800000
4  DC000D20221209012022100019                                        None
5              DC011D20221209  CG0019000300080220100264401000000000000000
6              DC011D20221209  CG0019000300080220400885101000039990700000
7              DC011D20221209  CG0019000300080220400885101000040013000000