python - 仅缩放数据框中包含字符串的数值

Question

我在 python 中，我尝试缩放数据框

subject_id hour_measure         urinecolor   blood pressure                  
3          1.00                 red          40
           1.15                 red          high
4          2.00              yellow          low

因为它包含数字和文本列下面的代码给了我错误

 #MinMaxScaler for Data
scaler = MinMaxScaler(copy=True, feature_range=(0, 1))
X = scaler.fit_transform(X)

它给了我错误，因为数据帧包含字符串，我如何告诉Python只缩放包含数字的列，并缩放字符串列中的数值。

Answer 1

将非数值转换为缺失值，然后使用 alternative solution为了缩放，最后将缺失值替换回原始值:

print (df)
   subject_id  hour_measure urinecolor blood pressure
0           3          1.00        red             40
1           3          1.15        red           high
2           4          2.00     yellow            low
3           5          5.00     yellow            100

df = df.set_index('subject_id')

df1 = df.apply(lambda x: pd.to_numeric(x, errors='coerce'))
df2 = (df1 - df1.min()) / (df1.max() - df1.min())

df = df2.combine_first(df)
print (df)
            hour_measure urinecolor blood pressure
subject_id                                        
3                 0.0000        red              0
3                 0.0375        red           high
4                 0.2500     yellow            low
5                 1.0000     yellow              1

<小时/>

第一个解决方案:

我建议通过字典将文本列替换为数字，例如:

dbp = {'high': 150, 'low': 60}

df['blood pressure'] = df['blood pressure'].replace(dbp)

一起:

#if subject_id are numeric convert them to index
df = df.set_index('subject_id')

dbp = {'high': 150, 'low': 60}
#replace to numbers and convert to integers
df['blood pressure'] = df['blood pressure'].replace(dbp).astype(int)

print (df)
            hour_measure urinecolor  blood pressure
subject_id                                         
3                   1.00        red              40
3                   1.15        red             150
4                   2.00     yellow              60

print (df.dtypes)
hour_measure      float64
urinecolor         object
blood pressure      int32
dtype: object

<小时/>

from sklearn import preprocessing

scaler = preprocessing.MinMaxScaler(copy=True, feature_range=(0, 1))
#select only numeric columns
X = scaler.fit_transform(df.select_dtypes(np.number))
print (X)
[[0.         0.        ]
 [0.15       1.        ]
 [1.         0.18181818]]

详细信息:

print (df.select_dtypes(np.number))
            hour_measure  blood pressure
subject_id                              
3                   1.00              40
3                   1.15             150
4                   2.00              60