python - Pandas 交叉表 : Change Order of Columns That Are Named as Formatted Dates (mmm yy)

Question

我一直在寻找如何为 pandas 交叉表排序列，但无济于事。我特别需要根据日期值对格式化日期 (mmm yy) 的列进行排序，而不是根据月份的 3 个字母名称 (mmm) 按字母顺序排序。

这是我的代码的详细信息:

Python 3.3

Pandas 0.12.0

f_dtflt 是一个 pandas 数据框。

f_dtflt.COLLECTION_DATE 是 dtype datetime64[ns]

我的交叉表语句是:

pd.crosstab(f_dtflt.EW_REGIONCOLLSITE, f_dtflt.COLLECTION_DATE.apply(lambda x: x.strftime("%b %y")), margins=True)

输出为:

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

我希望这些列按升序日期排序...10 月 12 日、11 月 12 日、...1 月 13 日、...9 月 13 日。我认识到我可以将日期格式化为 yy-mm(例如 13-01)，但这些标签将在报告中使用，这是我希望不要做出的妥协。

我是 python 和 pandas 的新手，所以请通过连接您的回答中的任何点来帮助新手!非常感谢。

<小时/>

方法一

编辑回应@Andy 答案的第一部分。第 3 步有问题:

我已尝试实现 Andy 的建议，以下是有关此工作的更多信息。

1) 我运行了以下行来查看日期。以下行创建诸如“2012-10”之类的值作为收集日期。 (通过打印“美化”？)

print(pd.DatetimeIndex(f_dtflt['COLLECTION_DATE']).to_period('M'))

2) 当上述语句输入交叉表时，它会将月份值更改为数字，例如 513、514 等(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，它会抛出一个错误，指出“int”对象没有属性“strftime”

table1.columns = table1.columns.map(lambda x: x.strftime("%b %y"))

我对此进行了相当多的研究，以下是我的一些笔记:

# 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)这确实适用于标记交叉表中的列:

table1.columns.name = 'COLLECTION_DATE'

<小时/>

方法2

@Andy 给出了第二个建议，我尝试了一下，但无法让它发挥作用。问题的很大一部分是我对 python、pandas 和 numpy 不熟悉。当我试图整理它时，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)

Answer 1

我从一个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 中对交叉表中的列进行排序的通用方法。它也可能适用于数据透视表，但我没有测试它，也没有查看细节。我想它也可以用于排序行标签，但我没有尝试这样做。

这将创建一个带有列标签的交叉表，例如“12 10_Oct 12”和 12 11_Nov 12”。该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“_”和我想要使​​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 "

函数和调用:

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 table ):

"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  "