Hey I'm over this problem for 2 hours, can someone explain why I get this error? I'm supposed to turn strings values (that presents 10,000 columbs of 3 states and a gender) to numeric values and don't know what's the problem I saw someone on Udemy do it and it worked fine.

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

Error:

Input In [15], in <cell line: 1>()
----> 1 X_train = sc.fit_transform(X_train)

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\sklearn\base.py:867, in TransformerMixin.fit_transform(self, X, y, **fit_params)
    863 # non-optimized default implementation; override when a better
    864 # method is possible for a given clustering algorithm
    865 if y is None:
    866     # fit method of arity 1 (unsupervised transformation)
--> 867     return self.fit(X, **fit_params).transform(X)
    868 else:
    869     # fit method of arity 2 (supervised transformation)
    870     return self.fit(X, y, **fit_params).transform(X)

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\sklearn\preprocessing\_data.py:809, in StandardScaler.fit(self, X, y, sample_weight)
    807 # Reset internal state before fitting
    808 self._reset()
--> 809 return self.partial_fit(X, y, sample_weight)

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\sklearn\preprocessing\_data.py:844, in StandardScaler.partial_fit(self, X, y, sample_weight)
    812 """Online computation of mean and std on X for later scaling.
    813 
    814 All of X is processed as a single batch. This is intended for cases
   (...)
    841     Fitted scaler.
    842 """
    843 first_call = not hasattr(self, "n_samples_seen_")
--> 844 X = self._validate_data(
    845     X,
    846     accept_sparse=("csr", "csc"),
    847     dtype=FLOAT_DTYPES,
    848     force_all_finite="allow-nan",
    849     reset=first_call,
    850 )
    851 n_features = X.shape[1]
    853 if sample_weight is not None:

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\sklearn\base.py:577, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)
    575     raise ValueError("Validation should be done on X, y or both.")
    576 elif not no_val_X and no_val_y:
--> 577     X = check_array(X, input_name="X", **check_params)
    578     out = X
    579 elif no_val_X and not no_val_y:

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\sklearn\utils\validation.py:856, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)
    854         array = array.astype(dtype, casting="unsafe", copy=False)
    855     else:
--> 856         array = np.asarray(array, order=order, dtype=dtype)
    857 except ComplexWarning as complex_warning:
    858     raise ValueError(
    859         "Complex data not supported\n{}\n".format(array)
    860     ) from complex_warning

ValueError: could not convert string to float: 'Spain'

CodePudding user response：

You need to encode your string columns (categorical features) first. Use OrdinalEncoder(), LabelEncoder() or OneHotEncoder() to convert categorical columns to numeric. You can only scale numerical variables.

CodePudding user response：

Ok I figured it out.

# Preform label encoding for gender variable
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
lableencoder_X_2 = LabelEncoder()
X[:, 2] = lableencoder_X_2.fit_transform(X[:, 2])

# preform one ho encoding for geography varaible
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([('ohe', OneHotEncoder(), [1])], remainder='passthrough')
X = np.array(ct.fit_transform(X), dtype = np.str)
X = X[:, 1:]