Pandas filter data frame rows by function

Question

I want to filter a data frame by more complex function based on different values in the row.

Is there a possibility to filter DF rows by a boolean function like you can do it e.g. in ES6 filter function?

Extreme simplified example to illustrate the problem:

import pandas as pd  def filter_fn(row):     if row['Name'] == 'Alisa' and row['Age'] > 24:         return False      return row  d = {     'Name': ['Alisa', 'Bobby', 'jodha', 'jack', 'raghu', 'Cathrine',              'Alisa', 'Bobby', 'kumar', 'Alisa', 'Alex', 'Cathrine'],     'Age': [26, 24, 23, 22, 23, 24, 26, 24, 22, 23, 24, 24],      'Score': [85, 63, 55, 74, 31, 77, 85, 63, 42, 62, 89, 77]}  df = pd.DataFrame(d, columns=['Name', 'Age', 'Score'])  df = df.apply(filter_fn, axis=1, broadcast=True)  print(df) 

I found something using apply() bit this actually returns only False/True filled rows using a bool function, which is expected.

My workaround would be returning the row itself when the function result would be True and returning False if not. But this would require a additional filtering after that.

        Name    Age  Score 0      False  False  False 1      Bobby     24     63 2      jodha     23     55 3       jack     22     74 4      raghu     23     31 5   Cathrine     24     77 6      False  False  False 7      Bobby     24     63 8      kumar     22     42 9      Alisa     23     62 10      Alex     24     89 11  Cathrine     24     77 

Answer 1

I think using functions here is unnecessary. It is better and mainly faster to use boolean indexing:

m = (df['Name'] == 'Alisa') & (df['Age'] > 24) print(m) 0      True 1     False 2     False 3     False 4     False 5     False 6      True 7     False 8     False 9     False 10    False 11    False dtype: bool  #invert mask by ~ df1 = df[~m] 

For more complicated filtering, you could use a function which must return a boolean value:

def filter_fn(row):     if row['Name'] == 'Alisa' and row['Age'] > 24:         return False     else:         return True  df = pd.DataFrame(d, columns=['Name', 'Age', 'Score']) m = df.apply(filter_fn, axis=1) print(m) 0     False 1      True 2      True 3      True 4      True 5      True 6     False 7      True 8      True 9      True 10     True 11     True dtype: bool  df1 = df[m]