我正在尝试使用sklearn热编码器对状态执行热编码。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：
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目的是将分类信息（通常是字符串）转换为数字...为什么说不能将字符串转换为浮点型呢？
最佳答案
熊猫数据框具有内置选项，可使用get_dummies method创建一种热编码。
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：
加利福尼亚州
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
关于python - 一种使用sklearn的国家/地区热编码，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/44361869/