How to use 'sklearn labelencoder' - Python

How to use 'sklearn labelencoder' in Python

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uber/causalml

103 def fit_transform(self, X, y=None):
104     """Encode categorical columns into label encoded columns
105 
106     Args:
107         X (pandas.DataFrame): categorical columns to encode
108 
109     Returns:
110         X (pandas.DataFrame): label encoded columns
111     """
112 
113     self.label_encoders = [None] * X.shape[1]
114     self.label_maxes = [None] * X.shape[1]
115 
116     for i, col in enumerate(X.columns):
117         self.label_encoders[i], self.label_maxes[i] = \
118             self._get_label_encoder_and_max(X[col])
119 
120         X.loc[:, col] = X[col].fillna(NAN_INT).map(self.label_encoders[i]).fillna(0)
121 
122     return X

automl/auto-sklearn

48 def fit_transform(self, X, y=None):
49     is_sparse = scipy.sparse.issparse(X)
50     X = self._fit(X)
51     if is_sparse:
52         return X
53     elif isinstance(X, np.ndarray):
54         return X
55     else:
56         return X.toarray()

103	def fit_transform(self, X, y=None):
104	"""Encode categorical columns into label encoded columns
105
106	Args:
107	X (pandas.DataFrame): categorical columns to encode
108
109	Returns:
110	X (pandas.DataFrame): label encoded columns
111	"""
112
113	self.label_encoders = [None] * X.shape[1]
114	self.label_maxes = [None] * X.shape[1]
115
116	for i, col in enumerate(X.columns):
117	self.label_encoders[i], self.label_maxes[i] = \
118	self._get_label_encoder_and_max(X[col])
119
120	X.loc[:, col] = X[col].fillna(NAN_INT).map(self.label_encoders[i]).fillna(0)
121
122	return X

48	def fit_transform(self, X, y=None):
49	is_sparse = scipy.sparse.issparse(X)
50	X = self._fit(X)
51	if is_sparse:
52	return X
53	elif isinstance(X, np.ndarray):
54	return X
55	else:
56	return X.toarray()

How to use 'sklearn labelencoder' in Python

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