如何在pandas中使用用户自定义的function根据列值和Timestamp返回一个值

Question

I have two dataframe, which I have joined. On the joined Dataframe, I'm writing a user-defined function where based on Timestamp and the value count of the column i need to return the value based on the condition mentioned below create a new column called "Day_Sentiment". But I'm getting below error. Please let me know how to go about it

Input:

                  Date          Content    Cleaned-content   Sentiment   
                  11/12/2020    abb        bbc abb           Bad         
                  12/10/2020    xyz        xxy               Good        
                  11/24/2020    tyu        yuu               Neutral     
                  12/16/2020    iop        yui               Bad         

Output:

               Date          Content    Cleaned-content   Sentiment   Day_Sentiment
               11/12/2020    abb        bbc abb           Bad         Bad
               12/10/2020    xyz        xxy               Good        Bad
               11/24/2020    tyu        yuu               Neutral     Bad
               12/16/2020    iop        yui               Bad         Bad

So far I tried below:

df = input_data.join(results)

def compare_def(df):

    no.bad_senti= df.loc[df['Sentiment'] == 'Bad']
    no.neut_senti = df.loc[df['Sentiment'] == 'Neutral']
    no.good_senti= df.loc[df['Sentiment'] == 'Good']

    if ((no.bad_senti> no.good_senti) & (no.bad_senti> no.neut_senti)):
       output = 'Bad'
    elif ((no.good_senti> no.bad_senti) & (no.good_senti> no.neut_senti)):
       output= 'Good'
    elif ((no.neut_senti> no.bad_senti) & (no.neut_senti> no.good_senti)):
       output= 'Neutral'
    elif no.good_senti== no.bad_senti:
       output= 'Neutral'
    elif no.bad_senti== no.neut_senti:
       output= 'bad'
    elif no.good_senti== no.neut_senti:
       output= 'good'
    else:
       output= 'Neutral'

    return output

df['Day_Sentiment'] = output

Alternate:

 output = compare_def(df)
 df['Day_Sentiment'] = output

Error:

     ValueError: Can only compare identically-labeled DataFrame objects

Example 1: Predicted Sentiments Sentiment 2 bad 1 good 1 Neutral

Then in function 2 > 1 and 2 > 1 returns Bad

Example 2: Sentiment: 2 bad 5 good 5 neutral

Function:

2 > 5 false 5 > 2 and 5 > 5 false 5 > 2 and 5 > 5 false 5==2 false 2==5 false 5==5 True return good

Answer 1

There Several issues with your code. To begin the variables bad, good, & neut are Panda Series of different lengths containing string variables. You then attempt to evaluate perform several conditional tests for example if ((bad> good) & (bad> neut) which generates your ValueError. I am not quite sure what logic you are attempting to implement, but the following template may help:

def compare_data(row):
    value = 'Good'
    # The logic here escapes me
    # Evaluate the row contents of row[Sentiment] and modify value
    return value  

df["Day Sentiment"]= df.apply(lambda row: compare_data(row), axis= 1)

Yields:

    Date    Content Cleaned-content Sentiment   Day Sentiment
0   11/12/2020  abb bbc abb Bad Good
1   12/10/2020  xyz xxy Good    Good
2   11/24/2020  tyu yuu Neutral Good
3   12/16/2020  iop yui Bad Good