integer#
- skrub.selectors.integer()[source]#
Select columns that have an integer data type.
This selects integer columns but not 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_ object dtype: object
>>> s.select(df, s.integer()) i64 I64 i8 0 2 2 3
Use s.boolean() to also select Boolean columns:
>>> s.select(df, s.integer() | s.boolean()) i64 I64 i8 bool_ Bool_ 0 2 2 3 True True