12. 使用布尔值、整数、标签进行选取

#  读取movie，根据布尔条件选取
 In[89]: movie = pd.read_csv('data/movie.csv', index_col='movie_title')
         c1 = movie['content_rating'] == 'G'
         c2 = movie['imdb_score'] < 4
         criteria = c1 & c2
 In[90]: movie_loc = movie.loc[criteria]
         movie_loc.head()
Out[90]:

#  检查loc条件和布尔条件创建出来的两个DataFrame是否一样
 In[91]: movie_loc.equals(movie[criteria])
Out[91]: True

#  尝试用.iloc使用布尔索引
 In[92]: movie_iloc = movie.iloc[criteria]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-92-24a12062c6c3> in <module>()
----> 1 movie_iloc = movie.iloc[criteria]
/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py in __getitem__(self, key)
   1326         else:
   1327             key = com._apply_if_callable(key, self.obj)
-> 1328             return self._getitem_axis(key, axis=0)
   1329 
   1330     def _is_scalar_access(self, key):
/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
   1731 
   1732         if is_bool_indexer(key):
-> 1733             self._has_valid_type(key, axis)
   1734             return self._getbool_axis(key, axis=axis)
   1735 
/Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py in _has_valid_type(self, key, axis)
   1588                                               "indexing on an integer type "
   1589                                               "is not available")
-> 1590                 raise ValueError("iLocation based boolean indexing cannot use "
   1591                                  "an indexable as a mask")
   1592             return True
ValueError: iLocation based boolean indexing cannot use an indexable as a mask

#  但是，却可以使用布尔值得ndarray，用values可以取出array
 In[93]: movie_iloc = movie.iloc[criteria.values]
 In[94]: movie_iloc.equals(movie_loc)
Out[94]: True
 In[95]: movie.loc[criteria.values]
Out[95]:

#  布尔索引也可以用来选取列
 In[96]: criteria_col = movie.dtypes == np.int64
         criteria_col.head()
Out[96]: color                      False
         director_name              False
         num_critic_for_reviews     False
         duration                   False
         director_facebook_likes    False
         dtype: bool

 In[97]: movie.loc[:, criteria_col].head()
Out[97]:

#  因为criteria_col是包含行索引的一个Series，必须要使用底层的ndarray，才能使用，iloc
 In[98]: movie.iloc[:, criteria_col.values].head()
Out[98]:

#  选取'content_rating', 'imdb_score', 'title_year', 'gross'四列，按照imdb_score升序排列
 In[99]: cols = ['content_rating', 'imdb_score', 'title_year', 'gross']
         movie.loc[criteria, cols].sort_values('imdb_score')
Out[99]:

#  用get_loc获取这四列的整数位置
 In[100]: col_index = [movie.columns.get_loc(col) for col in cols]
          col_index
Out[100]: [20, 24, 22, 8]

#  这时候就可以使用iloc了
 In[101]: movie.iloc[criteria.values, col_index].sort_values('imdb_score')
Out[101]:

原理

#  查看Series的底层结构
 In[102]: a = criteria.values
          a[:5]
Out[102]: array([False, False, False, False, False], dtype=bool)
 In[103]: len(a), len(criteria)
Out[103]: (4916, 4916)

更多

#  传入的布尔索引可以跟要操作的DataFrame长度不同
 In[104]: movie.loc[[True, False, True], [True, False, False, True]]
Out[104]: