pandas cut：如何将分类标签转换为字符串（否则无法导出到 Excel）？
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pandas cut: how to convert categorical labels to strings (otherwise cannot export to Excel)?
提问by Pythonista anonymous
I use pandas.cut() to discretise a continuous variable into a range, and then group by the result.
我使用 pandas.cut() 将连续变量离散到一个范围内，然后按结果分组。
After a lot of swearing because I couldn't figure out what was wrong, I have learnt that, if I don't supply custom labels to the cut() function, but rely on the default, then the output cannot be exported to excel. If I try this:
由于无法弄清楚出了什么问题，经过多次咒骂后，我了解到，如果我不为 cut() 函数提供自定义标签，而是依赖默认值，那么输出将无法导出到 excel . 如果我试试这个：
import pandas as pd
import numpy as np
writer = pd.ExcelWriter('test.xlsx')
wk = writer.book.add_worksheet('Test')
df= df= pd.DataFrame(np.random.randint(1,10,(10000,5)), columns=['a','b','c','d','e'])
df['range'] = pd.cut( df['a'],[-np.inf,3,8,np.inf] )
grouped=df.groupby('range').sum()
grouped.to_excel(writer, 'Export')
writer.close()
I get:
我得到：
raise TypeError("Unsupported type %s in write()" % type(token))
TypeError: Unsupported type <class 'pandas._libs.interval.Interval'> in write()
which it took me a while to decypher.
If instead I do assign labels:
如果我确实分配了标签：
df['range'] = pd.cut( df['a'],[-np.inf,3,8,np.inf], labels =['<3','3-8','>8'] )
then it all runs fine. Any suggestions on how to handle this without assigning custom labels? In the initial phase of my work I tend not to assign labels, because I still don't know how many bins I want - it's a trial and error approach, and assigning labels at each attempt would be time-consuming.
然后一切正常。关于如何在不分配自定义标签的情况下处理此问题的任何建议？在我工作的初始阶段，我倾向于不分配标签，因为我仍然不知道我想要多少个 bin - 这是一种反复试验的方法，每次尝试分配标签都非常耗时。
I am not sure if this can count as a bug, but at the very least it seems like a poorly documented annoyance!
我不确定这是否可以算作一个错误，但至少它似乎是一个记录不足的烦恼！
回答by Scott Boston
Use astype(str):
使用astype(str)：
writer = pd.ExcelWriter('test.xlsx')
wk = writer.book.add_worksheet('Test')
df= df= pd.DataFrame(np.random.randint(1,10,(10000,5)), columns=['a','b','c','d','e'])
df['range'] = pd.cut( df['a'],[-np.inf,3,8,np.inf] ).astype(str)
grouped=df.groupby('range').sum()
grouped.to_excel(writer, 'Export')
writer.close()
Output in excel:
excel输出：
range a b c d e
(-inf, 3.0] 6798 17277 16979 17266 16949
(3.0, 8.0] 33150 28051 27551 27692 27719
(8.0, inf] 9513 5153 5318 5106 5412
