熊貓：類型錯誤：浮置（）參數必須是一個字符串或數字

Question

我有一個包含

user_id date  browser conversion test sex age country 
    1 2015-12-03  IE  1   0 M 32.0 US 

這是我整個的代碼迄今一個數據幀！

data["country"].fillna("missing") 
data["age"].fillna(-10000, inplace=True) 
data["ads_channel"].fillna("missing") 
data["sex"].fillna("missing") 
data['date'] = pd.to_datetime(data.date) 

columns = data.columns.tolist() 
columns = [c for c in columns if c not in ["test"]] 
from sklearn import tree 
clf = tree.DecisionTreeClassifier(max_depth=2, min_samples_leaf = (len(data)/100)) 
clf = clf.fit(data[columns],data["test"]) 

我收到此錯誤：

--------------------------------------------------------------------------- 
TypeError         Traceback (most recent call last) 
<ipython-input-560-95a8a54aa939> in <module>() 
     4 from sklearn import tree 
     5 clf = tree.DecisionTreeClassifier(max_depth=2, min_samples_leaf = (len(data)/100)) 
----> 6 clf = clf.fit(data[columns],data["test"]) 

C:\Users\SnehaPriya\Anaconda2\lib\site-packages\sklearn\tree\tree.pyc in fit(self, X, y, sample_weight, check_input, X_idx_sorted) 
    152   random_state = check_random_state(self.random_state) 
    153   if check_input: 
--> 154    X = check_array(X, dtype=DTYPE, accept_sparse="csc") 
    155    if issparse(X): 
    156     X.sort_indices() 

C:\Users\SnehaPriya\Anaconda2\lib\site-packages\sklearn\utils\validation.pyc 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) 
    371          force_all_finite) 
    372  else: 
--> 373   array = np.array(array, dtype=dtype, order=order, copy=copy) 
    374 
    375   if ensure_2d: 

TypeError: float() argument must be a string or a number 

我仍然在學習代碼，我想知道如何克服這種錯誤。 任何幫助將不勝感激！

Answer 1

IIUC你需要排除列date也：

columns = [c for c in columns if c not in ["test", 'date']] 

因爲錯誤：

TypeError: float() argument must be a string or a number, not 'Timestamp'