我有看起来像这样的数据
shift_id user_id status organization_id location_id department_id open_positions city zip role_id specialty_id latitude longitude years_of_experience
2 9 S 1 1 19 1 brooklyn 48001 2 9 42.643 -82.583
6 60 S 12 19 20 1 test 68410 3 7 40.608 -95.856
9 61 S 12 19 20 1 new york 48001 1 7 42.643 -82.583
10 60 S 12 19 20 1 test 68410 3 7 40.608 -95.856
21 3 S 1 1 19 1 pune 48001 1 2 46.753 -89.584 0
4 7 S 1 1 19 1 needham 2494 4 4 42.292 -71.246 2
所以它包含字符串和数字特征。
我首先要执行特征消除，然后对其执行 SVM。
这是我的代码。
dataset = pd.read_csv("prod_data_for_ML.csv",header = 0)
#Data Pre-processing
data = dataset.drop('organization_id',1)
#data = data.drop('status',1)
#data = data.drop('city',1)
#Find median for features having NaN
median_zip, median_role_id, median_specialty_id, median_latitude, median_longitude = data['zip'].median(),data['role_id'].median(),data['specialty_id'].median(),data['latitude'].median(),data['longitude'].median()
data['zip'].fillna(median_zip, inplace=True)
data['role_id'].fillna(median_role_id, inplace=True)
data['specialty_id'].fillna(median_specialty_id, inplace=True)
data['latitude'].fillna(median_latitude, inplace=True)
data['longitude'].fillna(median_longitude, inplace=True)
#Fill YearOFExp with 0
data['years_of_experience'].fillna(0, inplace=True)
target = dataset.location_id
#Perform Recursive Feature Extraction
svm = SVR(kernel="linear")
rfe = RFE(svm, 5, step=1)
rfe = rfe.fit(data, target)
print(rfe.n_features_)
print(rfe.support_)
但是作为列status并且city具有字符串值，它给出 -
ValueError: could not convert string to float: 'S'
具有这样的字符串特征是显而易见的。处理这种情况的标准做法是什么？