我正在尝试使用 Pandas 的 df.get_dummies(( 对几个分类列进行单热编码，但它返回了一个我不明白的错误。 错误显示ValueError: could not convert string to float: 'Warm Cool'. 可能导致此问题的原因是什么，如何使用dtype == object成功对所有列进行独热编码？
我的数据集来自此处找到的DC_Properties.CSV文件。
我的代码和错误消息：
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Import packages section
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import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
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Read data section
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df = pd.read_csv('DC_Properties.csv', index_col=0)
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Preprocess data section
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# remove rows without sales prices
df = df[df.PRICE.notnull()]
# create month sold column
df['MONTHSOLD'] = [i[:i.find('/')] if type(i) == str else i for i in df.SALEDATE]
# create year sold column
df['YEARSOLD'] = [i[-4:] if type(i) == str else i for i in df.SALEDATE]
# join GBA and Living GBA
df['GBA'] = df['GBA'].fillna(df['LIVING_GBA'])
# remove unused columns
unused_cols = ['SALEDATE',
'GIS_LAST_MOD_DTTM',
'CMPLX_NUM',
'LIVING_GBA',
'FULLADDRESS',
'CITY',
'STATE',
'NATIONALGRID',
'ASSESSMENT_SUBNBHD',
'CENSUS_TRACT',
'CENSUS_BLOCK',
'X',
'Y']
df = df.drop(unused_cols, axis=1)
# one-hot encode categorical variables
pd.get_dummies(df, dummy_na=True)
# standardize the data
scaler = StandardScaler()
dataset = scaler.fit_transform(df)
# specify x and y variables
x = dataset[:,-y_idx]
y = dataset[:,'PRICE']
# split data into a train and test set
np.random.seed(123)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-81-62c3931b3dfa> in <module>
33 # standardize the data
34 scaler = StandardScaler()
---> 35 dataset = scaler.fit_transform(df)
36
37 # specify x and y variables
~Anaconda3libsite-packagessklearnbase.py in fit_transform(self, X, y, **fit_params)
551 if y is None:
552 # fit method of arity 1 (unsupervised transformation)
--> 553 return self.fit(X, **fit_params).transform(X)
554 else:
555 # fit method of arity 2 (supervised transformation)
~Anaconda3libsite-packagessklearnpreprocessingdata.py in fit(self, X, y)
637 # Reset internal state before fitting
638 self._reset()
--> 639 return self.partial_fit(X, y)
640
641 def partial_fit(self, X, y=None):
~Anaconda3libsite-packagessklearnpreprocessingdata.py in partial_fit(self, X, y)
661 X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
662 estimator=self, dtype=FLOAT_DTYPES,
--> 663 force_all_finite='allow-nan')
664
665 # Even in the case of `with_mean=False`, we update the mean anyway
~Anaconda3libsite-packagessklearnutilsvalidation.py 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, warn_on_dtype, estimator)
494 try:
495 warnings.simplefilter('error', ComplexWarning)
--> 496 array = np.asarray(array, dtype=dtype, order=order)
497 except ComplexWarning:
498 raise ValueError("Complex data not supportedn"
~Anaconda3libsite-packagesnumpycore_asarray.py in asarray(a, dtype, order)
83
84 """
---> 85 return array(a, dtype, copy=False, order=order)
86
87
ValueError: could not convert string to float: 'Warm Cool'
实际上是 StandardScaler 抛出错误，因为它遇到字符串。
原因是您使用的是 pd.dummies，但从未分配返回的数据帧。
# one-hot encode categorical variables
pd.get_dummies(df, dummy_na=True) # <------ is lost
要修复它，请将其更改为：
# one-hot encode categorical variables
df = pd.get_dummies(df, dummy_na=True)
我不认为你的onehotencoder(get_dummies(正在做你认为的事情。
将其替换为此行，或明确说明您希望get_dummies执行的操作(提及需要 onehotencoding 的列(，然后删除原始列。
df = pd.concat([df,pd.get_dummies(df, prefix='dummy',drop_first = False, dummy_na=True)],axis=1)
如果要按照某些统计方法的要求删除其中一个假人，请更改"drop_first = True"。
希望有帮助。祝你好运！