我是python的新手并且正在尝试使用pandas atm。 我知道有很多关于使用索引和列转换数据帧的答案。 但是，到目前为止，我还没有找到答案，这有助于我解决以下问题。 我有一个看起来像这样的datafram：
style VALUE GROWTH QUALITY
factor EarningsYield OPER_MARGIN RETURN_COM_EQY GEARING
VEDG LX Equity NaN 18.604873 NaN 1.04020
DPW DU Equity 0.057845 36.001430 10.957723 0.438649e
我想要达到的目标是：
ticker style factor value
VEDG LX Equity VALUE EarningsYield NaN
VEDG LX Equity GROWTH OPER_MARGIN 18.604873
VEDG LX Equity QUALITY RETURN_COM_EQY NaN
VEDG LX Equity QUALITY GEARING 1.04020
DPW DU Equity VALUE EarningsYield 0.057845
DPW DU Equity GROWTH OPER_MARGIN 36.001430
DPW DU Equity QUALITY RETURN_COM_EQY 10.957723
DPW DU Equity QUALITY GEARING 0.438649e
任何帮助都不仅仅是值得赞赏的。 请注意，自动收报机列当前是索引。
先谢谢你们。
啦啦队
感谢您的回答。到目前为止我所尝试的是以下内容：
我已经导入了一个excel文件，然后是：
# Load the xls file's Sheet1 as a dataframe
df = xls_file.parse('Sheet1')
style_names = np.array(['VALUE','GROWTH','QUALITY','QUALITY'])
factor_names = np.array([ 'EarningsYield ', 'OPER_MARGIN ' ,'RETURN_COM_EQY','GEARING'])
df.columns = [style_names,factor_names]
df.columns.names = ['style','factor']
print(df)
style VALUE GROWTH QUALITY
factor EarningsYield OPER_MARGIN RETURN_COM_EQY GEARING
VEDG LX Equity NaN 18.604873 NaN 1.040200
DPW DU Equity 0.057946 36.001430 10.957723 0.438649
SVST LI Equity NaN 25.405680 41.356272 0.306917
STM IM Equity 0.016426 3.068980 7.371885 0.227296
NYR BB Equity -0.334866 -5.012305 -32.771536 0.509514
MDC LN Equity 0.000400 13.168425 NaN 0.324293
TIT IM Equity 0.110168 19.563732 6.842755 0.574045
OCI NA Equity -0.002449 15.971676 12.469365 0.751047
BESI NA Equity 0.031403 20.024775 33.685981 0.263089
IMPN SW Equity 0.041195 2.808368 6.870435 0.390823
MHG NO Equity 0.009682 26.454333 29.083558 0.324450
IAG LN Equity 0.001430 11.450348 42.105263 0.586250
DG FP Equity 0.057341 10.504060 16.673043 0.496945
z = zscore(df)
z[z>3]=3
z[z<-3]=-3
print(z.unstack())
style factor
index 0 VEDG LX Equity
1 DPW DU Equity
2 SVST LI Equity
3 STM IM Equity
4 NYR BB Equity
5 MDC LN Equity
6 TIT IM Equity
7 OCI NA Equity
8 BESI NA Equity
9 IMPN SW Equity
10 MHG NO Equity
11 IAG LN Equity
12 DG FP Equity
VALUE EarningsYield 0 NaN
1 0.509544
2 NaN
3 0.150815
4 -2.88433
5 0.0123503
6 0.960737
7 -0.0122652
8 0.280218
9 0.364819
10 0.0925452
11 0.0212482
12 0.504316
GROWTH OPER_MARGIN 0 0.305012
1 1.87814
2 0.919993
3 -1.09986
...
9 -1.12343
10 1.01482
11 -0.341955
12 -0.427525
QUALITY RETURN_COM_EQY 0 NaN
1 -0.232745
2 1.20556
3 -0.402409
4 -2.30179
5 NaN
6 -0.427444
7 -0.161222
8 0.842639
9 -0.426135
10 0.624876
11 1.241
12 0.0376744
GEARING 0 2.49189
1 -0.18156
2 -0.76701
3 -1.12087
4 0.133384
5 -0.689786
6 0.420179
7 1.20682
8 -0.961795
9 -0.39411
10 -0.68909
11 0.474417
12 0.0775232
因此，当它看起来更接近我需要的时候，它看起来并不像Luce在做的时候所得到的。
为什么会这样？
谢谢Gerrit
更新：
我想我已经明白了：
df = xls_file.parse('Sheet1')
style_names = np.array(['VALUE','GROWTH','QUALITY','QUALITY'])
factor_names = np.array([ 'EarningsYield ', 'OPER_MARGIN ' ,'RETURN_COM_EQY','GEARING'])
df.columns = [style_names,factor_names]
df.columns.names = ['style','factor']
print(df)
z = zscore(df)
z[z>3]=3
z[z<-3]=-3
print(z)
print(z.unstack())
zu = z.unstack()
zur = zu.reset_index()
print(zur)
现在返回：
style factor level_2 0
0 VALUE EarningsYield VEDG LX Equity NaN
1 VALUE EarningsYield DPW DU Equity 0.509544
2 VALUE EarningsYield SVST LI Equity NaN
3 VALUE EarningsYield STM IM Equity 0.150815
4 VALUE EarningsYield NYR BB Equity -2.884326
5 VALUE EarningsYield MDC LN Equity 0.012350
6 VALUE EarningsYield TIT IM Equity 0.960737
7 VALUE EarningsYield OCI NA Equity -0.012265
8 VALUE EarningsYield BESI NA Equity 0.280218
9 VALUE EarningsYield IMPN SW Equity 0.364819
10 VALUE EarningsYield MHG NO Equity 0.092545
11 VALUE EarningsYield IAG LN Equity 0.021248
12 VALUE EarningsYield DG FP Equity 0.504316
13 GROWTH OPER_MARGIN VEDG LX Equity 0.305012
14 GROWTH OPER_MARGIN DPW DU Equity 1.878142
15 GROWTH OPER_MARGIN SVST LI Equity 0.919993
16 GROWTH OPER_MARGIN STM IM Equity -1.099862
17 GROWTH OPER_MARGIN NYR BB Equity -1.830634
18 GROWTH OPER_MARGIN MDC LN Equity -0.186593
19 GROWTH OPER_MARGIN TIT IM Equity 0.391720
20 GROWTH OPER_MARGIN OCI NA Equity 0.066898
21 GROWTH OPER_MARGIN BESI NA Equity 0.433411
22 GROWTH OPER_MARGIN IMPN SW Equity -1.123429
23 GROWTH OPER_MARGIN MHG NO Equity 1.014821
24 GROWTH OPER_MARGIN IAG LN Equity -0.341955
25 GROWTH OPER_MARGIN DG FP Equity -0.427525
26 QUALITY RETURN_COM_EQY VEDG LX Equity NaN
27 QUALITY RETURN_COM_EQY DPW DU Equity -0.232745
28 QUALITY RETURN_COM_EQY SVST LI Equity 1.205558
29 QUALITY RETURN_COM_EQY STM IM Equity -0.402409
30 QUALITY RETURN_COM_EQY NYR BB Equity -2.301789
31 QUALITY RETURN_COM_EQY MDC LN Equity NaN
32 QUALITY RETURN_COM_EQY TIT IM Equity -0.427444
33 QUALITY RETURN_COM_EQY OCI NA Equity -0.161222
34 QUALITY RETURN_COM_EQY BESI NA Equity 0.842639
35 QUALITY RETURN_COM_EQY IMPN SW Equity -0.426135
36 QUALITY RETURN_COM_EQY MHG NO Equity 0.624876
37 QUALITY RETURN_COM_EQY IAG LN Equity 1.240996
38 QUALITY RETURN_COM_EQY DG FP Equity 0.037674
39 QUALITY GEARING VEDG LX Equity 2.491893
40 QUALITY GEARING DPW DU Equity -0.181560
41 QUALITY GEARING SVST LI Equity -0.767010
42 QUALITY GEARING STM IM Equity -1.120867
43 QUALITY GEARING NYR BB Equity 0.133384
44 QUALITY GEARING MDC LN Equity -0.689786
45 QUALITY GEARING TIT IM Equity 0.420179
46 QUALITY GEARING OCI NA Equity 1.206820
47 QUALITY GEARING BESI NA Equity -0.961795
48 QUALITY GEARING IMPN SW Equity -0.394110
49 QUALITY GEARING MHG NO Equity -0.689090
50 QUALITY GEARING IAG LN Equity 0.474417
51 QUALITY GEARING DG FP Equity 0.077523
感谢您指出我正确的方向！
非常感谢
答案 0 :(得分：0)
看起来您正试图转置矩阵。以下是使用pandas实现此目的的一种方法：
`failed assertion `(length - offset)(12) must be >= 16 at buffer binding at index 0 for color[0].'`
答案 1 :(得分：0)
Unstack可用于将宽格式转换为长格式（相反的函数为pivot）。
>> import pandas as pd
>> from io import StringIO
>> csv = StringIO(u'''style VALUE GROWTH QUALITY QUALITY
factor EarningsYield OPER_MARGIN RETURN_COM_EQY GEARING
VEDG LX Equity NaN 18.604873 NaN 1.04020
DPW DU Equity 0.057845 36.001430 10.957723 0.438649e''')
>> df = pd.read_csv(csv, sep='\t', header=[0, 1], index_col=0)
>> df
style VALUE GROWTH QUALITY
factor EarningsYield OPER_MARGIN RETURN_COM_EQY GEARING
VEDG LX Equity NaN 18.604873 NaN 1.04020
DPW DU Equity 0.057845 36.001430 10.957723 0.438649e
>> df.unstack()
style factor
VALUE EarningsYield VEDG LX Equity NaN
DPW DU Equity 0.057845
GROWTH OPER_MARGIN VEDG LX Equity 18.6049
DPW DU Equity 36.0014
QUALITY RETURN_COM_EQY VEDG LX Equity NaN
DPW DU Equity 10.9577
GEARING VEDG LX Equity 1.04020
DPW DU Equity 0.438649e
要获得您的确切格式，您必须玩一下。