result[(result.CREATE_TIME > pd.to_datetime('2018-07')) & (result.CREATE_TIME < pd.to_datetime('2018-08'))]

如果要使用與（and），用符號&表示，如df.A > 2 & df.B < 3，需要加括號(df.A > 2) & (df.B < 3)，因為與（&）的優先順序高。

http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing

Boolean indexing

Another common operation is the use of boolean vectors to filter the data. The operators are: | for or, & for and, and ~for not. These must be grouped by using parentheses, since by default Python will evaluate an expression such as df.A > 2 & df.B < 3

Using a boolean vector to index a Series works exactly as in a NumPy ndarray:

In [147]: s = pd.Series(range(-3, 4))

In [148]: s
Out[148]: 
0   -3
1   -2
2   -1
3    0
4    1
5    2
6    3
dtype: int64

In [149]: s[s > 0]
Out[149]: 
4    1
5    2
6    3
dtype: int64

In [150]: s[(s < -1) | (s > 0.5)]
Out[150]: 
0   -3
1   -2
4    1
5    2
6    3
dtype: int64

In [151]: s[~(s < 0)]
Out[151]: 
3    0
4    1
5    2
6    3
dtype: int64
 

You may select rows from a DataFrame using a boolean vector the same length as the DataFrame’s index (for example, something derived from one of the columns of the DataFrame):

In [152]: df[df['A'] > 0]
Out[152]: 
                   A         B         C         D   E   0
2000-01-01  0.469112 -0.282863 -1.509059 -1.135632 NaN NaN
2000-01-02  1.212112 -0.173215  0.119209 -1.044236 NaN NaN
2000-01-04  7.000000 -0.706771 -1.039575  0.271860 NaN NaN
2000-01-07  0.404705  0.577046 -1.715002 -1.039268 NaN NaN

List comprehensions and map method of Series can also be used to produce more complex criteria:

In [153]: df2 = pd.DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
   .....:                     'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
   .....:                     'c' : np.random.randn(7)})
   .....: 

# only want 'two' or 'three'
In [154]: criterion = df2['a'].map(lambda x: x.startswith('t'))

In [155]: df2[criterion]
Out[155]: 
       a  b         c
2    two  y  0.041290
3  three  x  0.361719
4    two  y -0.238075

# equivalent but slower
In [156]: df2[[x.startswith('t') for x in df2['a']]]
Out[156]: 
       a  b         c
2    two  y  0.041290
3  three  x  0.361719
4    two  y -0.238075

# Multiple criteria
In [157]: df2[criterion & (df2['b'] == 'x')]
Out[157]: 
       a  b         c
3  three  x  0.361719

With the choice methods Selection by Label, Selection by Position, and Advanced Indexing you may select along more than one axis using boolean vectors combined with other indexing expressions.

In [158]: df2.loc[criterion & (df2['b'] == 'x'),'b':'c']
Out[158]: 
   b         c
3  x  0.361719