2. 构建多个布尔条件

  1.  In[11]: movie = pd.read_csv('data/movie.csv', index_col='movie_title')
  2.          movie.head()
  3. Out[11]:

2. 构建多个布尔条件 - 图1

  1. #  创建多个布尔条件
  2.  In[12]: criteria1 = movie.imdb_score > 8
  3.          criteria2 = movie.content_rating == 'PG-13'
  4.          criteria3 = (movie.title_year < 2000) | (movie.title_year >= 2010)
  5.          criteria2.head()
  6. Out[12]: movie_title
  7.          Avatar                                         True
  8.          Pirates of the Caribbean: At World's End       True
  9.          Spectre                                        True
  10.          The Dark Knight Rises                          True
  11.          Star Wars: Episode VII - The Force Awakens    False
  12.          Name: content_rating, dtype: bool
  1. #  将这些布尔条件合并成一个
  2.  In[13]: criteria_final = criteria1 & criteria2 & criteria3
  3.          criteria_final.head()
  4. Out[13]: movie_title
  5.          Avatar                                         False
  6.          Pirates of the Caribbean: At World's End       False
  7.          Spectre                                        False
  8.          The Dark Knight Rises                          True
  9.          Star Wars: Episode VII - The Force Awakens     False
  10.          Name: content_rating, dtype: bool

更多

  1. #  在Pandas中，位运算符（&, |, ~）的优先级高于比较运算符，因此如过前面的条件3不加括号，就会报错
  2.  In[14]: movie.title_year < 2000 | movie.title_year > 2009
  3. ---------------------------------------------------------------------------
  4. TypeError                                 Traceback (most recent call last)
  5. /Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/ops.py in na_op(x, y)
  6.     882         try:
  7. --> 883             result = op(x, y)
  8.     884         except TypeError:
  10. /Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/ops.py in <lambda>(x, y)
  11.     130                                    names('rand_'), op('&')),
  12. --> 131                  ror_=bool_method(lambda x, y: operator.or_(y, x),
  13.     132                                   names('ror_'), op('|')),
  15. TypeError: ufunc 'bitwise_or' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
  17. During handling of the above exception, another exception occurred:
  19. ValueError                                Traceback (most recent call last)
  20. /Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/ops.py in na_op(x, y)
  21.     900                         y = bool(y)
  22. --> 901                     result = lib.scalar_binop(x, y, op)
  23.     902                 except:
  25. pandas/_libs/lib.pyx in pandas._libs.lib.scalar_binop (pandas/_libs/lib.c:15035)()
  27. ValueError: Buffer dtype mismatch, expected 'Python object' but got 'double'
  29. During handling of the above exception, another exception occurred:
  31. TypeError                                 Traceback (most recent call last)
  32. <ipython-input-14-1e7ee3f1401c> in <module>()
  33. ----> 1 movie.title_year < 2000 | movie.title_year > 2009
  35. /Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/ops.py in wrapper(self, other)
  36.     933                       is_integer_dtype(np.asarray(other)) else fill_bool)
  37.     934             return filler(self._constructor(
  38. --> 935                 na_op(self.values, other),
  39.     936                 index=self.index)).__finalize__(self)
  40.     937 
  42. /Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/ops.py in na_op(x, y)
  43.     903                     raise TypeError("cannot compare a dtyped [{0}] array with "
  44.     904                                     "a scalar of type [{1}]".format(
  45. --> 905                                         x.dtype, type(y).__name__))
  46.     906 
  47.     907         return result
  49. TypeError: cannot compare a dtyped [float64] array with a scalar of type [bool]