CODEWITHSUNDEEP
pythonpandasdataframe
ola
ola

Reputation: 77

Can I use pandas loc method to select multiple columns and replace the resulting row values with NaN

I have a DataFrame like this:

students = {'ID': [2, 3, 5, 7, 11, 13], 
        'Name':['John','Jane','Sam','James','Stacy','Mary'],
        'Gender':['M','F','F','M','F','F'],
        'school_name':['College2','College2','College10','College2','College2','College2'],
        'grade':['9th','10th','9th','9th','8th','5th'],
        'math_score':[90,89,88,89,89,90],
        'art_score':[90,89,89,78,90,94]}
        
        students_df = pd.DataFrame(students)

Can I use the loc method on the students_df to select all the math_scores and art_scores from the 9th grade at College2 and replace them with NaN? Is there a clean way of doing this without breaking the process into two parts: one for the subsetting and the other for the replacing?

I tried to select this way:

students_df.loc[(students_df['school_name'] == 'College2') & (students_df['grade'] == "9th"),['grade','school_name','math_score','art_score']]

I replaced this way:

students_df['math_score'] = np.where((students_df['school_name']=='College2') & (students_df['grade']=='9th'), np.NaN, students_df['math_score'])

Can I achieve the same thing in a much cleaner and efficient way using loc and np.NaN?

Upvotes: 2

Views: 583

Answers (1)

jezrael
jezrael

Reputation: 862731

Select columns for replace missing values first and set NaN:

students_df.loc[(students_df['school_name'] == 'College2') & (students_df['grade'] == "9th"),['math_score','art_score']] = np.nan
print (students_df)
   ID   Name Gender school_name grade  math_score  art_score
0   2   John      M    College2   9th         NaN        NaN
1   3   Jane      F    College2  10th        89.0       89.0
2   5    Sam      F   College10   9th        88.0       89.0
3   7  James      M    College2   9th         NaN        NaN
4  11  Stacy      F    College2   8th        89.0       90.0
5  13   Mary      F    College2   5th        90.0       94.0

Upvotes: 1

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