在學習數據挖掘與蟒蛇CH5錯誤

Question

代碼：

import os 
import pandas as pd 
data_folder = os.path.join(os.path.expanduser("~"),"data","Ads") 
data_filename = os.path.join(data_folder,"ad.data") 
def convert_number(x): 
    try: 
     return float(x) 
    except ValueError: 
     return np.nan 
from collections import defaultdict 
converters = defaultdict(convert_number) 
converters[1558] = lambda x:1 if x.strip() == "ad." else 0 
ads = pd.read_csv(data_filename,header=None,converters=converters) 
ads[:5] 
x = ads.drop(1558,axis=1).values 
y = ads[1558] 
from sklearn.decomposition import PCA 
pca = PCA(n_components=5) 
xd = pca.fit_transform(x) 
import numpy as np 
np.set_printoptions(precision=3,suppress=True) 
pca.explained_variance_ratio_ 

錯誤：

ValueError        Traceback (most recent call last) 
<ipython-input-10-f726f2ff6f29> in <module>() 
     1 from sklearn.decomposition import PCA 
     2 pca = PCA(n_components=5) 
----> 3 xd = pca.fit_transform(x) 
     4 import numpy as np 
     5 np.set_printoptions(precision=3,suppress=True) 

/home/kongnian/anaconda3/lib/python3.5/site-packages/sklearn/decomposition/pca.py in fit_transform(self, X, y) 
    239 
    240   """ 
--> 241   U, S, V = self._fit(X) 
    242   U = U[:, :self.n_components_] 
    243 

/home/kongnian/anaconda3/lib/python3.5/site-packages/sklearn/decomposition/pca.py in _fit(self, X) 
    266    requested. 
    267   """ 
--> 268   X = check_array(X) 
    269   n_samples, n_features = X.shape 
    270   X = as_float_array(X, copy=self.copy) 

/home/kongnian/anaconda3/lib/python3.5/site-packages/sklearn/utils/validation.py 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: 

ValueError: could not convert string to float: '?' 

數據集： http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements下載廣告數據集 OS信息： 的Linux Ubuntu 4 .4.0-40-generic＃60-Ubuntu SMP Fri Sep 23 16:45:45 UTC 2016 x86_64 x86_64 x86_64 GNU/Linux

Answer 1

鏈接到的數據源包含?符號 - 我推測這些符號都是缺失值。我建議從CSV階段在讀取過程中對其進行過濾，像這樣：

ads = pd.read_csv(data_filename,header=None,converters=converters, na_values='?') 

你可以找到關於如何處理遺漏值在pandas docs