integer

skrub.selectors.integer()

Select columns that have an integer data type.

Boolean columns are not matched by this selector, only signed and unsigned ints are.

See also

numeric

Select all numeric columns (integer and float). Use this to select both integer and floating-point columns together.

float

Select floating-point columns only.

boolean

Select Boolean columns.

Examples

>>> from skrub import selectors as s
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame(
...     dict(
...         f64=[1.1],
...         F64=pd.Series([2.3]).convert_dtypes(),
...         i64=[2],
...         I64=pd.Series([2]).convert_dtypes(),
...         i8=np.int8(3),
...         bool_=[True],
...         Bool_=pd.Series([True]).convert_dtypes(),
...         str_=["hello"],
...     )
... )
>>> df
   f64  F64  i64  I64  i8  bool_  Bool_   str_
0  1.1  2.3    2    2   3   True   True  hello
>>> df.dtypes
f64      float64
F64      Float64
i64        int64
I64        Int64
i8          int8
bool_       bool
Bool_    boolean
str_      ...
dtype: object

Select all integer columns:

>>> s.select(df, s.integer())
   i64  I64  i8
0    2    2   3

Combine with boolean() to include Boolean columns:

>>> s.select(df, s.integer() | s.boolean())
   i64  I64  i8  bool_  Bool_
0    2    2   3   True   True