问题描述
我有一个数据框,其中列名是时间(0:00、0:10、0:20,...,23:50).现在,它们以字符串顺序排序(所以0:00是第一个,最后9:50是最后一个),但是我想在时间之后对其进行排序(所以0:00是第一个,而23:50是最后一个).
I have a dataframe where the column names are times (0:00, 0:10, 0:20, ..., 23:50). Right now, they're sorted in a string order (so 0:00 is first and 9:50 is last) but I want to sort them after time (so 0:00 is first and 23:50 is last).
如果时间是一列,则可以使用
If time is a column, you can use
df = df.sort(columns='Time',key=float)
但是1)仅当 time 本身是列而不是列名时才有效,并且2)不推荐使用sort(),因此我尝试放弃使用它.
But 1) that only works if time is a column itself, rather than the column names, and 2) sort() is deprecated so I try to abstain from using it.
我正在尝试使用
df = df.sort_index(axis = 1)
,但是由于列名是字符串格式,所以它们会根据字符串键进行排序.我已经尝试过
but since the column names are in string format, they get sorted according to a string key. I've tried
df = df.sort_index(key=float, axis=1)
但是会出现错误消息:
Traceback (most recent call last):
File "<ipython-input-112-5663f277da66>", line 1, in <module>
df.sort_index(key=float, axis=1)
TypeError: sort_index() got an unexpected keyword argument 'key'
有人对如何解决这个问题有想法吗?因此,烦恼的是sort_index()和sort_values()-没有关键参数!
Does anyone have ideas for how to fix this? So annoying that sort_index() - and sort_values() for that matter - don't have the key argument!!
推荐答案
尝试使用sorted
内置函数对列进行排序,并将输出传递到数据帧以进行索引.以下应作为工作示例:
Try sorting the columns with the sorted
builtin function and passing the output to the dataframe for indexing. The following should serve as a working example:
import pandas as pd
records = [(2, 33, 23, 45), (3, 4, 2, 4), (4, 5, 7, 19), (4, 6, 71, 2)]
df = pd.DataFrame.from_records(records, columns = ('0:00', '23:40', '12:30', '11:23'))
df
# 0:00 23:40 12:30 11:23
# 0 2 33 23 45
# 1 3 4 2 4
# 2 4 5 7 19
# 3 4 6 71 2
df[sorted(df,key=pd.to_datetime)]
# 0:00 11:23 12:30 23:40
# 0 2 45 23 33
# 1 3 4 2 4
# 2 4 19 7 5
# 3 4 2 71 6
我希望这对您有帮助
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