How to use 'sklearn labelencoder' in Python
Every line of 'sklearn labelencoder' code snippets is scanned for vulnerabilities by our powerful machine learning engine that combs millions of open source libraries, ensuring your Python code is secure.
All examples are scanned by Snyk Code
By copying the Snyk Code Snippets you agree to
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()