python - 切片不一致 [ :] behavior on Pandas Dataframes

Question

我有 2 个数据框。第一个数据框有数字作为索引。第二个数据框以日期时间作为索引。切片运算符 (:) 在这些数据帧上的行为不同。

案例一

>>> df = pd.DataFrame({'A':[1,2,3]}, index=[0,1,2])
>>> df
   A
0  1
1  2
2  3
>>> df [0:2]
   A
0  1
1  2

案例二

>>> a = dt.datetime(2000,1,1)
>>> b = dt.datetime(2000,1,2)
>>> c = dt.datetime(2000,1,3)
>>> df = pd.DataFrame({'A':[1,2,3]}, index = [a,b,c])
>>> df
            A
2000-01-01  1
2000-01-02  2
2000-01-03  3
>>> df[a:b]
            A
2000-01-01  1
2000-01-02  2

为什么最后一行在情况 1 中被排除在外，而在情况 2 中却没有？

Answer 1

不要使用它，最好使用 loc 来保持一致性:

df = pd.DataFrame({'A':[1,2,3]}, index=[0,1,2])

print (df.loc[0:2])
   A
0  1
1  2
2  3

a = datetime.datetime(2000,1,1)
b = datetime.datetime(2000,1,2)
c = datetime.datetime(2000,1,3)
df = pd.DataFrame({'A':[1,2,3]}, index = [a,b,c])

print (df.loc[a:b])
            A
2000-01-01  1
2000-01-02  2

为什么省略最后一行的原因可能在 docs 中找到:

With DataFrame, slicing inside of [] slices the rows. This is provided largely as a convenience since it is such a common operation.

print (df[0:2])
   A
0  1
1  2

用于按日期时间选择 exact indexing被使用:

... In contrast, indexing with Timestamp or datetime objects is exact, because the objects have exact meaning. These also follow the semantics of including both endpoints.