我一直在寻找如何订购pandas交叉表的列无济于事。我特别需要根据日期的值来订购格式化日期（mmm yy）的列，而不是按字母顺序排列在3个字母的月份名称（mmm）上。
以下是我的代码的详细信息：
python 3.3
pandas 0.12.0
f_dtflt是一个pandas数据帧。
f_dtflt.COLLECTION_DATE是dtype datetime64 [ns]
我的交叉表声明是：
pd.crosstab(f_dtflt.EW_REGIONCOLLSITE, f_dtflt.COLLECTION_DATE.apply(lambda x: x.strftime("%b %y")), margins=True)
输出结果为：
COLLECTION_DATE Apr 13 Aug 13 Dec 12 Feb 13 Jan 13 Jul 13 Jun 13
EW_REGIONCOLLSITE
EAST 1964 2092 2280 2272 2757 2113 1902
WEST 2579 2011 1003 2351 2216 1506 1823
All 4543 4103 3283 4623 4973 3619 3725
COLLECTION_DATE Mar 13 May 13 Nov 12 Oct 12 Sep 13 All
EW_REGIONCOLLSITE
EAST 1682 1981 2108 825 975 22951
WEST 2770 3014 407 42 888 20610
All 4452 4995 2515 867 1863 43561
我希望按照提升日期排序列... 10月12日，11月12日，... 1月13日，... 9月13日。 我知道我可以格式化日期，使它们是yy-mm（例如13-01），但这些标签将用于报告中，这是我希望不做的妥协。
我是python和pandas的新手，所以请通过连接回复中的任何点来帮助新手！非常感谢。
方法1
编辑以回应@Andy回答的第一部分。第3步出现问题：
我试图实施Andy的建议，这里有更多关于这项工作的信息。
1）我运行以下行来查看日期的样子。以下行为收集日期创建诸如“2012-10”之类的值。 （通过印刷“美化”）
print(pd.DatetimeIndex(f_dtflt['COLLECTION_DATE']).to_period('M'))
2）当在交叉表中输入上述语句时，它会将月份值更改为513,514等数字（字段中的实际值？）
table1=pd.crosstab(f_dtflt.EW_REGIONCOLLSITE, pd.DatetimeIndex(f_dtflt['COLLECTION_DATE']).to_period('M'), margins=True)
这是输出：
col_0 513 514 515 516 517 518 519 520 521 522
EW_REGIONCOLLSITE
EAST 825 2108 2280 2757 2272 1682 1964 1981 1902 2113
WEST 42 407 1003 2216 2351 2770 2579 3014 1823 1506
All 867 2515 3283 4973 4623 4452 4543 4995 3725 3619
col_0 523 524 All
EW_REGIONCOLLSITE
EAST 2092 975 22951
WEST 2011 888 20610
All 4103 1863 43561
3）当我运行以下代码时，它会抛出一个'int'对象没有属性'strftime'的错误
table1.columns = table1.columns.map(lambda x: x.strftime("%b %y"))
我玩了很多，这是我的一些笔记：
# This runs and creates an array of strings: '513' etc.
pd.to_datetime(table1.columns.map(str), unit='M')
# The last entry in table1.columns is "All" and needs to be removed. Hence [:-1] slice.
# This also runs but seems to give years in 1630's.
pd.DatetimeIndex(table1.columns[:-1].map(str)).to_datetime('M')
# This does not run because it says object is immutable
table1.columns[:-1]=pd.DatetimeIndex(table1.columns[:-1].map(str)).to_datetime('M')
# This also runs but the output is weird. It seems to give an array of both dates and -1
table1.columns.reindex(pd.DatetimeIndex(table1.columns[:-1].map(str)).to_datetime('M'))
# Does not run: DatetimeIndex() must be called with a collection of some kind, '513' was passed
table1.columns = table1.columns.map(lambda x: pd.DatetimeIndex(str(x)).strftime("%b %y"))
# Does not run: DatetimeIndex object is not callable
table1.rename(columns=pd.DatetimeIndex(table1.columns[:-1].map(str)).to_datetime('M'))
4）这适用于标记交叉表中的列：
table1.columns.name = 'COLLECTION_DATE'
方法2
@Andy提出了第二个建议，我玩弄了它，无法让它发挥作用。问题的一个重要部分是我对python，pandas和numpy缺乏熟悉。当我试图解决它时，我为自己做了笔记。以下是我的笔记：# Working with a new concept
# This creates row titles of 12 10, 12 11, etc.
table1=pd.crosstab(f_dtflt.EW_REGIONCOLLSITE, f_dtflt.COLLECTION_DATE.apply(lambda x: x.strftime("%y %m")), margins=True)
# This throws an error that yb is not defined
table1.columns.map(lambda yb: "%s %s" % (y, b) for y, b in yb.split())
# Tried to simplify and see what happens. Runs and creates an array of lists such as [['12, '10'], ['12', '11']...]
table1.columns.map(lambda x: x.split())
# Trying a different approach. This creates a numpy array of datetimes.
tempholder=table1.columns[:-1].map(lambda x: datetime.datetime(year=int(x[0:2]), month=int(x[3:]), day=1))
# Noted that f_dtflt['COLLECTION_DATE'] was a dtype of datetime64[ns] but tempholder was dtype object. So had issue.
# Convert to datetime64
# Get error: Out of bounds nanosecond timestamp: 12-10-01 00:00:00
tempholder=pd.to_datetime(tempholder)
# Tempholder is an array of datetimes from the datetime module. I used the pandas date function above.
# Need to change that and use python datetime module function.
# Does not work: 'numpy.ndarray' object has no attribute 'apply'...
# this is a pandas function which does not work on a numpy array.
tempholder.apply(lambda x: x.strftime('%b %y'))
# This works for numpy array but I can't tell what it contains.
# print(tempholder) gives <map object at 0x0000000026C04F28>
# tempholder gives Out[169]: <builtins.map at 0x26c04f28>
tempholder=map(lambda x: x.strftime('%b %y'), tempholder)
答案 0 :(得分：1)
我从稍微不同的角度解决了这个问题，并创建了一个函数，可以用作在pandas交叉表中对列进行排序的一般方法。它也适用于数据透视表，但我没有测试，也没看过细节。我想它也可以用来订购行标签，但我没有尝试过。
这会创建一个带有列标签的交叉表，例如“12 10_Oct 12”和12 11_Nov 12“。标签有效地强制交叉表的字母顺序对我有利。标签的字母顺序部分与”_“连接在一起我想要使用的标签。
table_1=pd.crosstab(f_dtflt.EW_REGIONCOLLSITE, f_dtflt.COLLECTION_DATE.apply(lambda x: x.strftime("%y %m_%b %y")), margins=True)
输出：
"COLLECTION_DATE 12 10_Oct 12 12 11_Nov 12 12 12_Dec 12 13 01_Jan 13
EW_REGIONCOLLSITE
EAST 825 2108 2280 2757
WEST 42 407 1003 2216
All 867 2515 3283 4973
COLLECTION_DATE 13 02_Feb 13 13 03_Mar 13 13 04_Apr 13 13 05_May 13
EW_REGIONCOLLSITE
EAST 2272 1682 1964 1981
WEST 2351 2770 2579 3014
All 4623 4452 4543 4995
COLLECTION_DATE 13 06_Jun 13 13 07_Jul 13 13 08_Aug 13 13 09_Sep 13
EW_REGIONCOLLSITE
EAST 1902 2113 2092 975
WEST 1823 1506 2011 888
All 3725 3619 4103 1863
COLLECTION_DATE All
EW_REGIONCOLLSITE
EAST 22951
WEST 20610
All 43561 "
功能和电话：
def clean_label(label_list, margins='False'):
''' This function takes the column index list from a crosstab (or pivot table?) in pandas and removes the
part of the label before and including the "_". This allows the user to order the columns manually by creating
an alphabetical index followed by "_" and then the label that they would like to use. For example, a label such as
['a_Positive', 'b_Negative'] will be converted to ['Positive', 'Negative']. Another example would be to order dates
in a table from ['12 10_Oct 12', '12 11_Nov 12'] to ['Oct 12', 'Nov 12']
margins = False if the crosstab was created without margins and therefore does not have an "All" at the end of the list
margins = True if the crosstab was created with margins and therefore has an "All" at the end of the list
'''
corrected_list=list()
# If one creates margins in pivot/crosstab, will get the last column of "All"
# This has to be removed from the following code or it will throw an error.
if margins:
convert_list = label_list[:-1]
else:
convert_list = label_list
for l in convert_list:
x,y=l.split('_')
corrected_list.append(y)
if margins:
corrected_list.append('Total') # Renames "All" to "Total"
return corrected_list
# Change the labels on the crosstab table
table_1.columns=clean_label(table_1.columns, margins=True)
# Change name of columns
table_1.columns.name = 'Month of Collection'
# Change name of rows
table_1.index.name = 'Region'
输出（决赛桌）：
"Month of Collection Oct 12 Nov 12 Dec 12 Jan 13 Feb 13 Mar 13 Apr 13
Region
EAST 825 2108 2280 2757 2272 1682 1964
WEST 42 407 1003 2216 2351 2770 2579
All 867 2515 3283 4973 4623 4452 4543
Month of Collection May 13 Jun 13 Jul 13 Aug 13 Sep 13 Total
Region
EAST 1981 1902 2113 2092 975 22951
WEST 3014 1823 1506 2011 888 20610
All 4995 3725 3619 4103 1863 43561 "
答案 1 :(得分：0)
如果你已经完成了一个字符串的年月（并且它的顺序正确），你可以逆转：
In [1]: df = pd.DataFrame([['a', 'b']], columns=['12 Mar', '12 Jun'])
In [2]: df.columns.map(lambda yb: ' '.join(reversed(yb.split())))
Out[2]: array(['Mar 12', 'Jun 12'], dtype=object)
In [3]: df.columns = df.columns.map(lambda yb: ' '.join(reversed(yb.split())))
我曾建议您可以使用句点执行此操作：
pd.DatetimeIndex(f_dtflt['COLLECTION_DATE']).to_period('M')
然后，您可以将列清理为所需的格式：
df.columns = df.columns.map(lambda x: x.strftime("%b %y"))
df.columns.name = 'COLLECTION_DATE'
但这似乎会将期间索引更改为int（可能是错误？）。