I have a DataFrame like this:

and Df2:

How can I check each row of df['Name'], see if the row contains a value of Df2['Palabra'], and in that case get the value of Df2['Marca'] and put it in the new column?. The result should be something like this:

Data:

df:

{'Casa': ['Solo Deportes', 'Solo Deportes', 'Solo Deportes', 'Solo Deportes'],
 'Name': ['Paleta De Padel Adidas Metalbone CTRL',
          'Zapatillas Running Under Armour Charged Stamin...',
          'Rompeviento Con Capucha Reebok Woven Azu',
          'Remera Michael Jordan Chicago Bulls']}

Df2:

{'Palabra': ['Acqualine', 'Addnice', 'Adnnice', 'Under Armour', 'Jordan', 'Adidas', 'Reebok'], 
 'Marca': ['Acqualine', 'Addnice', 'Addnice', 'Under Armour', 'Nike', 'Adidas', 'Reebok']}

CodePudding user response：

A simple solution is to use iterrows in a generator expression and next to iterate over Df2 and check if a matching item appears:

df1['Marca'] = df1['Name'].apply(lambda name: next((r['Marca'] for _, r in Df2.iterrows() if r['Palabra'] in name), float('nan')))

Output:

            Casa                                               Name         Marca
0  Solo Deportes              Paleta De Padel Adidas Metalbone CTRL        Adidas
1  Solo Deportes  Zapatillas Running Under Armour Charged Stamin...  Under Armour
2  Solo Deportes           Rompeviento Con Capucha Reebok Woven Azu        Reebok
3  Solo Deportes                Remera Michael Jordan Chicago Bulls          Nike