是否必须为顺序变量创建虚拟变量?转换时也会出错

只需将分类变量设置为 dtype="category" 不够,无法工作。

需要将类别值转换为真正的类别值 pd.factorize() ,其中每个类别都被分配一个数字标签。

比如说 df 是您的熊猫数据框。一般来说,您可以使用此样板代码:

df_numeric = df.select_dtypes(exclude=['object'])
df_obj = df.select_dtypes(include=['object']).copy()

# factorize categoricals columnwise
for c in df_obj:
     df_obj[c] = pd.factorize(df_obj[c])[0]

# if you want to one hot encode then add this line:
df_obj = pd.get_dummies(df_obj, prefix_sep='_', drop_first = True)

# merge dataframes back to one dataframe
df_final = pd.concat([df_numeric, df_obj], axis=1)

因为你的分类变量已经被分解了(据我所知),你可以跳过分解,尝试一个热编码。

另请参见 this post on stats.stackexchange .

如果要标准化/规范化数字数据(而不是类别数据),请使用以下函数:

from sklearn import preprocessing

def scale_data(data, scale="robust"):
    x = data.values     
    if scale == "minmax":
        scaler = preprocessing.MinMaxScaler()
        x_scaled = scaler.fit_transform(x)
    elif scale == "standard":
        scaler = preprocessing.StandardScaler()
        x_scaled = scaler.fit_transform(x)
    elif scale == "quantile":
        scaler = preprocessing.QuantileTransformer()
        x_scaled = scaler.fit_transform(x)      
    elif scale == "robust":
        scaler = preprocessing.RobustScaler()
        x_scaled = scaler.fit_transform(x)  
    data = pd.DataFrame(x_scaled, columns = data.columns) 
    return data

scaled_df = scale_data(df_numeric, "robust")

把它们放在一起 for your dataset :

from sklearn import preprocessing

df = pd.read_excel("default of credit card clients.xls", skiprows=1)

y = df['default payment next month'] #target variable
del df['default payment next month']

c = [2,3,4] # index of categorical data columns
r = list(range(0,24)) 
r = [x for x in r if x not in c] # get list of all other columns
df_cat = df.iloc[:, [2,3,4]].copy()
df_con = df.iloc[:, r].copy()

# factorize categorical data
for c in df_cat:
     df_cat[c] = pd.factorize(df_cat[c])[0]

# scale continuous data
scaler = preprocessing.MinMaxScaler()
df_scaled = scaler.fit_transform(df_con)
df_scaled = pd.DataFrame(df_scaled, columns=df_con.columns)

df_final = pd.concat([df_cat, df_scaled], axis=1)

#reorder columns back to original order
cols = df.columns
df_final = df_final[cols]

为了进一步改进代码,在标准化之前进行列车/测试拆分, fit_transform() 关于培训数据, transform() 在测试数据上。否则将出现数据泄漏。