On use of any method - pandas

Question

a code from Kaggle, which is said to remove outliners:
outliers_mask = (ft.abs() > ft.abs().quantile(outl_thresh)).any(axis=1)
Would not Any return a boolean item? either a an item being in a list or not?
So what the code says is, save in the mask all absolute values in Ft which are above the quantile (introduced by another variable)? What does the Any stand for? what for? thank you.

Answer 1

I think first part return DataFrame filled by boolean True or/and False:
(ft.abs() > ft.abs().quantile(outl_thresh))
so is added DataFrame.any for test if at least one True per rows to boolean Series.
df = pd.DataFrame({'a':[False, False, True],
'b':[False, True, True],
'c':[False, False, True]})
print (df)
a b c
0 False False False
1 False True False
2 True True True
print (df.any(axis=1))
0 False <- no True per rows
1 True <- one True per rows
2 True <- three Trues per rows
dtype: bool
Similar method for test if all values are Trues is DataFrame.all:
print (df.all(axis=1))
0 False
1 False
2 True
dtype: bool
Reason is for filtering by boolean indexing is necessary boolean Series, not boolean DataFrame.
Another sample data:
np.random.seed(2021)
ft = pd.DataFrame(np.random.randint(100, size=(10, 5))).sub(20)
print (ft)
0 1 2 3 4
0 65 37 -20 74 66
1 24 42 71 9 1
2 73 4 -8 50 50
3 13 -13 -19 77 6
4 46 28 79 43 29
5 -4 30 34 32 73
6 -15 29 18 -6 51
7 65 50 21 1 5
8 -10 16 -1 37 62
9 70 -5 20 56 33
outl_thresh = 0.95
print (ft.abs().quantile(outl_thresh))
0 71.65
1 46.40
2 75.40
3 75.65
4 69.85
Name: 0.95, dtype: float64
print((ft.abs() > ft.abs().quantile(outl_thresh)))
0 1 2 3 4
0 False False False False False
1 False False False False False
2 True False False False False
3 False False False True False
4 False False True False False
5 False False False False True
6 False False False False False
7 False True False False False
8 False False False False False
9 False False False False False
outliers_mask = (ft.abs() > ft.abs().quantile(outl_thresh)).any(axis=1)
print (outliers_mask)
0 False
1 False
2 True
3 True
4 True
5 True
6 False
7 True
8 False
9 False
dtype: bool
df1 = ft[outliers_mask]
print (df1)
0 1 2 3 4
2 73 4 -8 50 50
3 13 -13 -19 77 6
4 46 28 79 43 29
5 -4 30 34 32 73
7 65 50 21 1 5
0 1 2 3 4
df2 = ft[~outliers_mask]
print (df2)
0 1 2 3 4
0 65 37 -20 74 66
1 24 42 71 9 1
6 -15 29 18 -6 51
8 -10 16 -1 37 62
9 70 -5 20 56 33