사용한 코드
from sympy import Derivative, symbols
x = symbols('x') #x를 기호변수화
fx = x 2 - 10 * x 3 + 3
fprime = Derivative(fx, x).doit() #x에 대해서 미분
print("fx 의 도함수는 : ", fprime, "입니다")
n = fprime.subs({x: 1})
print("fx에서 x = 1 에서의 순간변화율(미분계수는) ", n , "입니다")
sklearn.compose.ColumnTransformer
class sklearn.compose.ColumnTransformer(transformers, *, remainder='drop', sparse_threshold=0.3, n_jobs=None, transformer_weights=None, verbose=False, verbose_feature_names_out=True)[source]
Applies transformers to columns of an array or pandas DataFrame.
This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each transformer will be concatenated to form a single feature space. This is useful for heterogeneous or columnar data, to combine several feature extraction mechanisms or transformations into a single transformer.
