为什么熊猫在这里应用lambda比循环慢？

Question

我有一个熊猫数据框，我想根据是否满足某些条件进行过滤。我跑了一个循环，.apply()然后用来%%timeit测试速度。数据集大约有45000行。循环的代码片段为：

%%timeit
qualified_actions = []
for row in all_actions.index:
    if all_actions.ix[row,'Lower'] <= all_actions.ix[row, 'Mid'] <= all_actions.ix[row,'Upper']:
        qualified_actions.append(True)
    else:
        qualified_actions.append(False)

每个循环1.44 s±3.7毫秒（平均±标准偏差，共7次运行，每个循环1次）

而且.apply()是：

%%timeit
qualified_actions = all_actions.apply(lambda row: row['Lower'] <= row['Mid'] <= row['Upper'], axis=1)

每个循环6.71 s±54.6 ms（平均±标准偏差，共7次运行，每个循环1次）

我认为.apply()应该比循环遍历大熊猫更快。有人可以解释为什么在这种情况下速度变慢吗？

Answer 1

apply在后台使用循环，因此，如果需要更好的性能，最好的和最快的方法是最好的选择。

没有循环，只有链2条件向量化解决方案：

m1 = all_actions['Lower'] <= all_actions['Mid']
m2 = all_actions['Mid'] <= all_actions['Upper']
qualified_actions = m1 & m2

感谢Jon Clements提供的另一种解决方案：

all_actions.Mid.between(all_actions.Lower, all_actions.Upper)

时间 ：

np.random.seed(2017)
N = 45000
all_actions=pd.DataFrame(np.random.randint(50, size=(N,3)),columns=['Lower','Mid','Upper'])

#print (all_actions)

---

In [85]: %%timeit
    ...: qualified_actions = []
    ...: for row in all_actions.index:
    ...:     if all_actions.ix[row,'Lower'] <= all_actions.ix[row, 'Mid'] <= all_actions.ix[row,'Upper']:
    ...:         qualified_actions.append(True)
    ...:     else:
    ...:         qualified_actions.append(False)
    ...: 
    ...: 
__main__:259: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated
1 loop, best of 3: 579 ms per loop

In [86]: %%timeit
    ...: (all_actions.apply(lambda row: row['Lower'] <= row['Mid'] <= row['Upper'], axis=1))
    ...: 
1 loop, best of 3: 1.17 s per loop

In [87]: %%timeit
    ...: ((all_actions['Lower'] <= all_actions['Mid']) & (all_actions['Mid'] <= all_actions['Upper']))
    ...: 
1000 loops, best of 3: 509 µs per loop


In [90]: %%timeit
    ...: (all_actions.Mid.between(all_actions.Lower, all_actions.Upper))
    ...: 
1000 loops, best of 3: 520 µs per loop