使用带有 IntervalIndex 的 pandas.cut 后如何重命名类别? [英] How to rename categories after using pandas.cut with IntervalIndex?

问题描述

我使用 pandas.cut 和由 IntervalIndex.from_tuples 创建的 bin 对数据框中的列进行了离散化.

I discretized a column in my dataframe using pandas.cut with bins created by IntervalIndex.from_tuples.

剪切按预期工作，但是类别显示为我在 IntervalIndex 中指定的元组.有没有办法将类别重命名为不同的标签，例如(小、中、大)?

The cut works as intended however the categories are shown as the tuples I specified in the IntervalIndex. Is there any way to rename the categories into a different label e.g. (Small, Medium, Large)?

示例:

bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
pd.cut([0, 0.5, 1.5, 2.5, 4.5], bins)

结果类别将是:

[NaN, (0, 1], NaN, (2, 3], (4, 5]]
Categories (3, interval[int64]): [(0, 1] < (2, 3] < (4, 5]]

我正在尝试将 [(0, 1] < (2, 3] < (4, 5]] 更改为 1, 2 ,3> 或 小、中、大.

I am trying to change [(0, 1] < (2, 3] < (4, 5]] into something like 1, 2 ,3 or small, medium ,large.

遗憾的是，在使用 IntervalIndex 时，pd.cut 的标签参数参数会被忽略.

Sadly, the labels parameter arguments of pd.cut is ignored when using IntervalIndex.

谢谢！

更新:

感谢@SergeyBushmanov，我注意到这个问题仅在尝试更改数据框内的类别标签时才存在(这正是我想要做的).更新示例:

Thanks to @SergeyBushmanov I noticed that this issue only exist when trying to change category labels inside a dataframe (which is what I am trying to do). Updated example:

In [1]: df = pd.DataFrame([0, 0.5, 1.5, 2.5, 4.5], columns = ['col1'])
In [2]: bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
In [3]: df['col1'] = pd.cut(df['col1'], bins)
In [4]: df['col1'].categories = ['small','med','large']

In [5]: df['col1']

Out [5]:
0       NaN
1    (0, 1]
2       NaN
3    (2, 3]
4    (4, 5]
Name: col1, dtype: category
Categories (3, interval[int64]): [(0, 1] < (2, 3] < (4, 5]]

推荐答案

如果我们有一些数据:

bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
x = pd.cut([0, 0.5, 1.5, 2.5, 4.5], bins)

您可以尝试重新分配类别，例如:

You may try re-assigning categories like :

In [7]: x.categories = [1,2,3]

In [8]: x   
Out[8]: 
[NaN, 1, NaN, 2, 3]
Categories (3, int64): [1 < 2 < 3]

或:

In [9]: x.categories = ["small", "medium", "big"]                         

In [10]: x                                             
Out[10]: 
[NaN, small, NaN, medium, big]
Categories (3, object): [small < medium < big]

更新:

df = pd.DataFrame([0, 0.5, 1.5, 2.5, 4.5], columns = ['col1'])
bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
x = pd.cut(df["col1"].to_list(),bins)
x.categories = [1,2,3]
df['col1'] = x
df.col1
0    NaN
1      1
2    NaN
3      2
4      3
Name: col1, dtype: category
Categories (3, int64): [1 < 2 < 3]

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