使用熊猫的数据透视表

Question

我有以下数据帧:

df1= df[['rsa_units','regions','ssno','veteran','pos_off_ttl','occ_ser','grade','gender','ethnicity','age','age_category','service_time','type_appt','disabled','actn_dt','nat_actn_2_3','csc_auth_12','fy']]

这将产生1.4百万的记录.我参加了第12场比赛.

Eastern Region (R9),Eastern Region (R9),123456789,Non Vet,LBRER,3502,3,Male,White,43.0,Older Gen X'ers,5.0,Temporary,,2009-05-18 00:00:00,115,BDN,2009
Northern Region (R1),Northern Region (R1),234567891,Non Vet,FRSTRY TECHNCN,0462,4,Male,White,37.0,Younger Gen X'ers,7.0,Temporary,,2007-05-27 00:00:00,115,BDN,2007
Northern Region (R1),Northern Region (R1),345678912,Non Vet,FRSTRY AID,0462,3,Male,White,33.0,Younger Gen X'ers,8.0,Temporary,,2006-06-05 00:00:00,115,BDN,2006
Northern Research Station (NRS),Research & Development(RES),456789123,Non Vet,FRSTRY TECHNCN,0462,7,Male,White,37.0,Younger Gen X'ers,10.0,Term,,2006-11-26 00:00:00,702,N6M,2007
Intermountain Region (R4),Intermountain Region (R4),5678912345,Non Vet,BIOLCL SCI TECHNCN,0404,5,Male,White,45.0,Older Gen X'ers,6.0,Temporary,,2008-05-18 00:00:00,115,BWA,2008
Intermountain Region (R4),Intermountain Region (R4),678912345,Non Vet,FRSTRY AID (FIRE),0462,3,Female,White,31.0,Younger Gen X'ers,5.0,Temporary,,2009-05-10 00:00:00,115,BDN,2009
Pacific Southwest Region (R5),Pacific Southwest Region (R5),789123456,Non Vet,FRSTRY AID (FIRE),0462,3,Male,White,31.0,Younger Gen X'ers,3.0,Temporary,,2012-05-06 00:00:00,115,NAM,2012
Pacific Southwest Region (R5),Pacific Southwest Region (R5),891234567,Non Vet,FRSTRY AID (FIRE),0462,3,Male,White,31.0,Younger Gen X'ers,3.0,Temporary,,2011-06-05 00:00:00,115,BDN,2011
Intermountain Region (R4),Intermountain Region (R4),912345678,Non Vet,FRSTRY TECHNCN,0462,5,Male,White,37.0,Younger Gen X'ers,11.0,Temporary,,2006-04-30 00:00:00,115,BDN,2006
Northern Region (R1),Northern Region (R1),987654321,Non Vet,FRSTRY TECHNCN,0462,4,Male,White,37.0,Younger Gen X'ers,11.0,Temporary,,2005-04-11 00:00:00,115,BDN,2005
Southwest Region (R3),Southwest Region (R3),876543219,Non Vet,FRSTRY TECHNCN (HOTSHOT/HANDCREW),0462,4,Male,White,30.0,Gen Y Millennial,4.0,Temporary,,2013-03-24 00:00:00,115,NAM,2013
Southwest Region (R3),Southwest Region (R3),765432198,Non Vet,FRSTRY TECHNCN (RECR),0462,4,Male,White,30.0,Gen Y Millennial,5.0,Temporary,,2010-11-21 00:00:00,115,BDN,2011

然后我在['nat_actn_2_3']上过滤某些招聘代码.

h1 = df1[df1['nat_actn_2_3'].isin(['100','101','108','170','171','115','130','140','141','190','702','703'])]
h2 = h1.sort('ssno')
h3 = h2.drop_duplicates(['ssno','actn_dt'])

并且可以查看value_counts()以查看按地区划分的总雇员数.

total_newhires = h3['regions'].value_counts()
total_newhires

生产:

Out[38]:
Pacific Southwest Region (R5)      42255
Pacific Northwest Region (R6)      32081
Intermountain Region (R4)          24045
Northern Region (R1)               22822
Rocky Mountain Region (R2)         17481
Southwest Region (R3)              17305
Eastern Region (R9)                11034
Research & Development(RES)         7337
Southern Region (R8)                7288
Albuquerque Service Center(ASC)     7032
Washington Office(WO)               4837
Alaska Region (R10)                 4210
Job Corps(JC)                       4010
nda                                  438

我想在excel中做一些事情,我可以将['regions']作为我的行,['fy']作为列,根据['ssno']给我一个总数每个['fy'].最终根据数字进行计算也很不错,比如平均值和总和.

除了查看网址中的示例:http: //pandas.pydata.org/pandas-docs/stable/reshaping.html之外,我还尝试过:

hirestable = pivot_table(h3, values=['ethnicity', 'veteran'], rows=['regions'], cols=['fy'])

我想知道groupby可能是我正在寻找的吗？

任何帮助表示赞赏.我在这上花了3天时间,似乎无法把它放在一起.

所以基于下面的答案我使用以下代码做了一个数据透视:

h3.pivot_table(values=['ssno'], rows=['nat_actn_2_3'], cols=['fy'], aggfunc=len).  

这产生了一些不错的结果.当我使用'种族'或'退伍军人'作为价值时,我的结果真的很奇怪,并且与我的价值数量不符.不确定枢轴是否消除重复或什么,但它没有正确出来.

ssno
fy  2005    2006    2007    2008    2009    2010    2011    2012    2013    2014    2015
nat_actn_2_3                                            
100  34  20  25  18  38  43  45  14  19  25  10
101  510     453     725     795     1029    1293    957     383     470     605     145
108  170     132     112     85  123     127     84  43  40  29  10
115  9203    8972    7946    9038    10139   10480   9211    8735    10482   11258   339
130  299     313     431     324     291     325     336     202     230     436     112
140  62  74  71  75  132     125     82  42  45  74  18
141  20  16  23  17  20  14  10  9   13  17  7
170  202     433     226     278     336     386     284     265     121     118     49
171  4771    4627    4234    4196    4470    4472    3270    3145    354     341     34
190  1   1   NaN     NaN     NaN     1   NaN     NaN     NaN     NaN     NaN
702  3141    3099    3429    3030    3758    3952    3813    2902    2329    2375    650
703  2280    2354    2225    2050    2260    2328    2172    2503    2649    2856    726

Answer 1

试试这样:

h3.pivot_table(values=['ethnicity', 'veteran'], index=['regions'], columns=['fy'], aggfunc=len, fill_value=0)

要获得计数,请使用 aggfunc = len

您的isin引用也是一个字符串列表,但您为列提供的数据'nat_actn_2_3'是int

尝试:

h3.pivot_table(values=['ethnicity', 'veteran'], rows=['regions'], cols=['fy'], aggfunc=len, fill_value=0)

如果您有旧版本的熊猫