使用for循环在范围之间筛选数据帧的列？

Question

我有一个这样的数据框：

+----------------------------------------------------------------------------------+
|       Total_Production                      Utilization_rate         Avg_Count   |
+----------------------------------------------------------------------------------+
| 0            6.503907                      96.027778                26.194017    |
| 9            6.171308                      95.638889                31.500943    |
| 18           6.144897                      95.986111                27.494776    |
| 27           6.056882                      95.916667                27.525495    |
| 36           6.107343                     105.541667                21.500208    |
| 45           2.139576                      96.166667                27.480307    |
| 54           6.161222                      96.486111                27.498256    |
| 63           1.034555                      56.388889                27.568885    |
| 72           5.021524                      91.069444                30.931702    |
| 81           5.831919                      96.277778                28.284872    |
| 90           2.689860                      62.486111                18.691440    |
| 99           5.227672                      95.555556                31.441761    |
| 108          1.465271                      95.541667                30.064098    |
+----------------------------------------------------------------------------------+

范围分为两个系列。 最高范围： 总产量7.744379 利用率104.534796 平均数29.691733

最低范围： 总产量3880623 利用率64.315015 平均数22.652148

筛选列数据的最佳方法是什么？ 我可以通过迭代行来使用for循环吗？

Answer 1

可以使用^{} operator限制单个列的范围：

df[ 
  (3.880623 < df['Total_Production'])  & (df['Total_Production'] < 7.744379) &
  (64.315015 < df['Utilization_rate']) & (df['Utilization_rate'] <  104.534796) &
  (22.652148 < df['Avg_Count'])        & (df['Avg_Count'] < 29.691733)        
]

Answer 2

def foo():
    df[ 
  (3.880623 < df['Total_Production'])  & (df['Total_Production'] < 7.744379) &
  (64.315015 < df['Utilization_rate']) & (df['Utilization_rate'] <  104.534796) &
  (22.652148 < df['Avg_Count'])        & (df['Avg_Count'] < 29.691733) ]

def foo1():
    df[df.Total_Production.between(left=3.880623, right=7.744379) &
       df.Utilization_rate.between(left=64.315015, right=104.534796) &
       df.Avg_Count.between(left=22.652148, right=29.691733)]

def foo2():
    df.query("3.880623 < Total_Production < 7.744379 and 64.315015 < Utilization_rate <  104.534796\
    and 22.652148 < Avg_Count < 29.691733")

%timeit foo()
%timeit foo1()
%timeit foo2()

输出：

100 loops, best of 3: 2.95 ms per loop
100 loops, best of 3: 2.92 ms per loop
100 loops, best of 3: 3.67 ms per loop

Answer 3

你可以用query

In [233]: df.query('3.880623 < Total_Production < 7.744379 and 64.315015 < Utiliza
     ...: tion_rate <  104.534796 and 22.652148 < Avg_Count < 29.691733')
Out[233]:
    Total_Production  Utilization_rate  Avg_Count
0           6.503907         96.027778  26.194017
18          6.144897         95.986111  27.494776
27          6.056882         95.916667  27.525495
54          6.161222         96.486111  27.498256
81          5.831919         96.277778  28.284872