How to use 'sklearn preprocessing labelencoder' in Python

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103def 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
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48def 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()

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