从一系列 pandas 间隔中提取左右限制

Question

我想获取具有 pandas 间隔的列的间隔边距，并将它们写在列 'left'、'right' 中。 Iterrows 不起作用（文档说它不会用于写入数据）而且无论如何它都不是更好的解决方案。

import pandas as pd

i1 = pd.Interval(left=85, right=94)
i2 = pd.Interval(left=95, right=104)
i3 = pd.Interval(left=105, right=114)
i4 = pd.Interval(left=115, right=124)
i5 = pd.Interval(left=125, right=134)
i6 = pd.Interval(left=135, right=144)
i7 = pd.Interval(left=145, right=154)
i8 = pd.Interval(left=155, right=164)
i9 = pd.Interval(left=165, right=174)

data = pd.DataFrame(
    {
    "intervals":[i1,i2,i3,i4,i5,i6,i7,i8,i9],
    "left"     :[0,0,0,0,0,0,0,0,0],
    "right"    :[0,0,0,0,0,0,0,0,0]
    },
    index=[0,1,2,3,4,5,6,7,8]
)

#this is not working (has no effect):
for index, row in data.iterrows():
    print(row.intervals.left, row.intervals.right)
    row.left = row.intervals.left
    row.right = row.intervals.right

我们怎样做：

data['left']=data['intervals'].left

data['right']=data['intervals'].right

谢谢！

Answer 1

根据您的间隔创建一个 pandas.IntervalIndex。然后您可以访问 .left 和 .right 属性。

import pandas as pd

idx = pd.IntervalIndex([i1, i2, i3, i4, i5, i6, i7, i8, i9])  
pd.DataFrame({'intervals': idx, 'left': idx.left, 'right': idx.right})

    intervals  left  right
0    (85, 94]    85     94
1   (95, 104]    95    104
2  (105, 114]   105    114
3  (115, 124]   115    124
4  (125, 134]   125    134
5  (135, 144]   135    144
6  (145, 154]   145    154
7  (155, 164]   155    164
8  (165, 174]   165    174

另一个选项是使用 map 和 operator.attrgetter（你看，没有 lambda...）：

from operator import attrgetter

df['left'] = df['intervals'].map(attrgetter('left'))
df['right'] = df['intervals'].map(attrgetter('right'))

df
    intervals left right
0    (85, 94]   85    94
1   (95, 104]   95   104
2  (105, 114]  105   114
3  (115, 124]  115   124
4  (125, 134]  125   134
5  (135, 144]  135   144
6  (145, 154]  145   154
7  (155, 164]  155   164
8  (165, 174]  165   174

Answer 2

A pandas.arrays.IntervalArray 是在类似 Series 的结构中存储区间数据的首选方式。

对于@coldspeed 的第一个例子，IntervalArray 基本上是替换的下降：

In [2]: pd.__version__
Out[2]: '1.1.3'

In [3]: ia = pd.arrays.IntervalArray([i1, i2, i3, i4, i5, i6, i7, i8, i9])

In [4]: df = pd.DataFrame({'intervals': ia, 'left': ia.left, 'right': ia.right})

In [5]: df
Out[5]:
    intervals  left  right
0    (85, 94]    85     94
1   (95, 104]    95    104
2  (105, 114]   105    114
3  (115, 124]   115    124
4  (125, 134]   125    134
5  (135, 144]   135    144
6  (145, 154]   145    154
7  (155, 164]   155    164
8  (165, 174]   165    174

如果您已经在 Series 或 DataFrame 中拥有间隔数据，@coldspeed 的第二个示例通过访问 array 属性变得更加简单：

In [6]: df = pd.DataFrame({'intervals': ia})

In [7]: df['left'] = df['intervals'].array.left

In [8]: df['right'] = df['intervals'].array.right

In [9]: df
Out[9]:
    intervals  left  right
0    (85, 94]    85     94
1   (95, 104]    95    104
2  (105, 114]   105    114
3  (115, 124]   115    124
4  (125, 134]   125    134
5  (135, 144]   135    144
6  (145, 154]   145    154
7  (155, 164]   155    164
8  (165, 174]   165    174

Answer 3

一个简单的方法是使用 apply() 方法：

    data['left'] = data['intervals'].apply(lambda x: x.left)
    data['right'] = data['intervals'].apply(lambda x: x.right)
    data

    intervals      left     right
    0   (85, 94]     85      94
    1   (95, 104]    95     104
    ...
    8   (165, 174]  165     174

从一系列 pandas 间隔中提取左右限制

Extract left and right limit from a Series of pandas Intervals

python

intervals

pandas