Different DataFrames need to compare and add new value

Question

I've two Dataframes

1. DF1 = Contains (~30 Columns and 15000 entries) relevant are "TYP" (string) and "VERSION" (string)
2. DF2 = Contains "SOFTWARE_NAME" (string), "VERSION" (string) and "EOS" (date)

Now I'd like to add in DF1 additional columns "EOS_DATE" (date), "EOS" (bool) if

  DF1['TYP'] == DF2['SOFTWARE_NAME'] &
  DF1['VERSION'] == DF2['VERSION']

then

  DF1['EOS_DATE'] = DF2['EOS']
  DF1['EOS'] = if DF2['EOS'] < now() then True else False

I've tried with np, pd.where(~) but running mainly in this issue:

ValueError: Can only compare identically-labeled Series objects

Answer 1

First, change your columns values, so you won't have any issue (my goal is to make only one dataframe)

DF2 = DF2.rename(columns={"EOS": "DF2_EOS", "VERSION": "DF2_VERSION"})

Then you want to apply a change only for lines valuing your conditions. The best to do is to merge your dataframes, keeping all your DF1 rows (ie : standard merge) :

DF1 = DF1.merge(DF2, left_on=['TYP', 'VERSION'], right_on=['SOFTWARE_NAME', 'DF2_VERSION'])

Finally make your changes :

DF1.loc[DF1['DF2_EOS'] < pandas.to_datetime('today').normalize(), 'EOS'] = True
DF1.loc[DF1['DF2_EOS'] >= pandas.to_datetime('today').normalize(), 'EOS'] = False
DF1 = DF1.rename(columns={"DF2_EOS": "EOF_DATE"})