python 3.x - 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]