Merge two dataframes groupby the column values of a dataframe

Question

I have the following dataframe:

df1 = pd.DataFrame({'Parent': ['Stay home', "Stay home","Stay home", 'Go outside', "Go Outside","Go outside"],
                    'Child' : ['Severe weather', "Severe weather", "Severe weather", 'Sunny', "Sunny", "sunny"]})

    Parent      Child
0   Stay home   Severe weather
1   Stay home   Severe weather
2   Stay home   Severe weather
3   Go outside  Sunny
4   Go Outside  Sunny
5   Go outside  sunny

And a second one:

df2 = pd.DataFrame({'Similarity_Score': ['SimilarityScore:0.43693185876069784', 'SimilarityScore:0.299807821163373']})

             Similarity_Score
0   SimilarityScore:0.43693185876069784
1   SimilarityScore:0.299807821163373

I want to merge the two dataframes based on the values of Child values of df1.

Expected outcome:

     Parent     Child           Similarity_Score
0   Stay home   Severe weather  0.43693185876069784
1   Stay home   Severe weather  0.43693185876069784
2   Stay home   Severe weather  0.43693185876069784
3   Go outside  Sunny           0.299807821163373
4   Go Outside  Sunny           0.299807821163373
5   Go outside  sunny           0.299807821163373

I tried the usual merge and concat methods but could not find the solution. Any ideas?

Answer 1

If you want to assign the scores based on the Child's value, you can do it like that:

import numpy as np
import pandas as pd

df1 = pd.DataFrame({'Parent': ['Stay home', "Stay home", "Stay home", 'Go outside', "Go Outside", "Go outside"],
                    'Child': ['Severe weather', "Severe weather", "Severe weather", 'Sunny', "Sunny", "Sunny"]})
df2 = pd.DataFrame({'Similarity_Score': ['SimilarityScore:0.43693185876069784', 'SimilarityScore:0.299807821163373']})

# Split the string at : and convert to float
df2['Similarity_Score'] = df2['Similarity_Score'].str.split(':').str[1].astype(float)

# calculate auxiliary column position to base the matching on
df1['position'] = df1['Child'].apply(lambda row: np.where(df1['Child'].unique() == row)[0][0])

# merge both dataframes and drop auxiliary column position
df = df1.merge(df2, left_on='position', right_index=True).drop(columns=["position"])

Answer 2

based on your responses, the merge is based on the index, after taking identifying unique values in df1

# identifying the group
df1['key']=df1.groupby(['Parent','Child']).ngroup(ascending=False)
df1

# merge the two DF, and while merging split the similarity scope to take only numeric part

(df1.merge(df2['Similarity_Score'].str.split(':', expand=True)[1],
          left_on='key', 
          right_index=True)
    .drop(columns='key'))

Parent  Child   1
0   Stay home   Severe weather  0.43693185876069784
1   Stay home   Severe weather  0.43693185876069784
2   Stay home   Severe weather  0.43693185876069784
3   Go outside  Sunny   0.299807821163373
4   Go outside  Sunny   0.299807821163373
5   Go outside  Sunny   0.299807821163373

Answer 3

Is there a specific parameter you want to join based of? Are the scores constant for for Sunny (~0.299) and Severe Weather? If that's the case then make a binary column and merge based on that.