I have 2 dataframes, first with the column of text data (more than 10k rows) and second with keywords (almost 100 list)
DataFrame 1:
Text
a white house cat plays in garden
cat is a domestic species of small carnivorous mammal
cat is walking in garden behind white house
yellow banana is healthy
DataFrame 2:
ID Keywords
1 ['cat','white']
2 ['garden','white','cat']
3 ['domestic','mammal']
I want to add column in datafram 1 with ID where maximum number of words matched with dataframe 2. Also, if there is tie between more than 1 or 2 ID's then concatinate both ID's together. There are some cases as well where None of the words matched therefore, Add 'No Match' in that case.
Output:
Text ID
a white house cat plays in garden 2
cat is a domestic species of small carnivorous mammal 3
cat is walking in behind white house 1,2
yellow banana is healthy 'No Match'
This will work. It creates a list of the number of matches per list of keywords, then looks up the ID
of the max values in that list.
import pandas as pd
import ast
df1 = pd.DataFrame(['a white house cat plays in garden', 'cat is a domestic species of small carnivorous mammal', 'cat is walking in behind white house', 'yellow banana is healthy'], columns=['Text'])
df2 = pd.DataFrame([ { "ID": 1, "Keywords": "['cat','white']" }, { "ID": 2, "Keywords": "['garden','white','cat']" }, { "ID": 3, "Keywords": "['domestic','mammal']" } ])
df2['Keywords'] = df2['Keywords'].apply(ast.literal_eval)
def get_ids(text):
matches = [len(set(text.split(" ")) & set(i)) for i in df2['Keywords']]
matches_ids = [df2['ID'][index] for index, val in enumerate(matches) if val == max(matches) if max(matches)>0 ]
return ", ".join(str(x) for x in matches_ids) if matches_ids else "No Match"
df1['ID'] = df1['Text'].apply(get_ids)
Result:
Text | ID | |
---|---|---|
0 | a white house cat plays in garden | 2 |
1 | cat is a domestic species of small carnivorous mammal | 3 |
2 | cat is walking in behind white house | 1, 2 |
3 | yellow banana is healthy | No Match |