Pandas Crosstab：更改命名為格式化日期的列的順序（mmm yy）

Question

我一直在尋找如何訂購pandas交叉表的列無濟於事。 我特別需要根據日期的值來訂購格式化日期（mmm yy）的列，而不是按字母順序在3個字母的月份名稱（mmm）上排序。

以下是我的代碼的詳細信息：

python 3.3

大熊貓0.12.0

f_dtflt是一個pandas數據幀。

f_dtflt.COLLECTION_DATE是f_dtflt.COLLECTION_DATE datetime64 [ns]

我的交叉表聲明是：

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

輸出是：

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

我希望按照升序日期排序列... 10月12日，11月12日，... 1月13日，... 9月13日。我認識到我可以格式化日期，使它們是yy-mm（例如13- 01）但這些標簽將用於報告中，這是我希望不做出的妥協。

我是python和pandas的新手，所以請通過連接你的回復中的任何點來幫助新手！ 謝謝一堆。

---

方法1

編輯以回應@Andy回答的第一部分。 第3步出現問題：

我試圖實現Andy的建議，這里有更多關於這項工作的信息。

1）我運行以下行來查看日期的樣子。 以下行為收集日期創建諸如“2012-10”之類的值。 （打印“美化”？）

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

2）當上述語句輸入交叉表時，它會將月份值更改為513,514等數字（字段中的實際值？）

table1=pd.crosstab(f_dtflt.EW_REGIONCOLLSITE, pd.DatetimeIndex(f_dtflt['COLLECTION_DATE']).to_period('M'), margins=True)

這是輸出：

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）當我運行以下代碼時，它會拋出一個'int'對象沒有屬性'strftime'的錯誤

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

我玩了很多，這是我的一些筆記：

# 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）這適用於標記交叉表中的列：

table1.columns.name = 'COLLECTION_DATE'

---

方法2

@Andy提出了第二個建議，我玩弄了它，無法讓它發揮作用。 問題的一個重要部分是我對python，pandas和numpy缺乏熟悉。 當我試圖解決它時，我為自己做了筆記。 這是我的筆記：

# 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

我從一個稍微不同的角度解決了這個問題，並創建了一個函數，可以用作在pandas交叉表中對列進行排序的一般方法。 它也適用於數據透視表，但我沒有測試，也沒看過細節。 我想它也可以用來訂購行標簽，但我沒有嘗試。

這會創建一個帶有列標簽的交叉表，例如“12 10_Oct 12”和12 11_Nov 12“。標簽有效地強制交叉表的字母順序對我有利。標簽的字母順序部分與”_“連接，標簽表示我想用。

table_1=pd.crosstab(f_dtflt.EW_REGIONCOLLSITE, f_dtflt.COLLECTION_DATE.apply(lambda x: x.strftime("%y %m_%b %y")), margins=True)

輸出：

"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 "

功能和調用：

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'

輸出（決賽桌）：

"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  "

Answer 2

如果你已經完成了作為字符串的年月（並且它的順序正確），你可以逆轉：

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())))

我曾建議你可以用句號做到這一點：

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

然后，您可以將列清理為您需要的格式：

df.columns = df.columns.map(lambda x: x.strftime("%b %y"))
df.columns.name = 'COLLECTION_DATE'

但這似乎將期間索引更改為int（可能是一個錯誤？）。