我正在尝试使用Sklearn -Hot编码器对状态进行热编码。这是我的熊猫数据框：
State
0 FL
1 CA
2 MD
3 NY
4 NY
5 NY
6 NY
我写道：
from sklearn.preprocessing import OneHotEncoder
enc=OneHotEncoder(sparse=False)
enc.fit(data)
这是错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-78-a0b336acd757> in <module>()
----> 1 enc.fit(data)
/anaconda/envs/env3_insight/lib/python3.6/site-packages/sklearn/preprocessing/data.py in fit(self, X, y)
1842 self
1843 """
-> 1844 self.fit_transform(X)
1845 return self
1846
/anaconda/envs/env3_insight/lib/python3.6/site-packages/sklearn/preprocessing/data.py in fit_transform(self, X, y)
1900 """
1901 return _transform_selected(X, self._fit_transform,
-> 1902 self.categorical_features, copy=True)
1903
1904 def _transform(self, X):
/anaconda/envs/env3_insight/lib/python3.6/site-packages/sklearn/preprocessing/data.py in _transform_selected(X, transform, selected, copy)
1695 X : array or sparse matrix, shape=(n_samples, n_features_new)
1696 """
-> 1697 X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)
1698
1699 if isinstance(selected, six.string_types) and selected == "all":
/anaconda/envs/env3_insight/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
380 force_all_finite)
381 else:
--> 382 array = np.array(array, dtype=dtype, order=order, copy=copy)
383
384 if ensure_2d:
ValueError: could not convert string to float: 'NY'
我不明白。我认为进行热编码的全部要点是从分类，经常字符串，信息转换为数字...为什么说我不能将字符串转换为float，那么？
pANDAS数据框架有一个使用get_dummies方法创建一个热编码的构建选项。
。在您的示例中：
data = pd.DataFrame(['FL','CA','MD','NY','NY','NY','NY'], columns= ['State'])
pd.get_dummies(data.State)
将导致：
ca fl md ny
0 0 1 0 0
1 1 0 0 0
2 0 0 1 0
3 0 0 0 1
4 0 0 0 1
5 0 0 0 1
6 0 0 0 1