How to use 'sklearn preprocessing labelencoder' in Python
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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()