Pandas: umformen Datenrahmen

Question

Ich habe folgende Datenrahmen:

url='https://raw.githubusercontent.com/108michael/ms_thesis/master/crsp.dime.mpl.df' 

zz=pd.read_csv(url) 
zz.head(5) 

    date feccandid feccandcfscore.dyn pacid paccfscore cid  catcode  type_x di amtsum state log_diff_unemployment party type_y bills years_exp disposition  billsum 
0 2006 S8NV00073 0.496 C00000422 0.330 N00006619 H1100 24K  D 5000 NV -0.024693 Republican rep  s22-109  12 support  3 
1 2006 S8NV00073 0.496 C00375360 0.176 N00006619 H1100 24K  D 4500 NV -0.024693 Republican rep  s22-109  12 support  3 
2 2006 S8NV00073 0.496 C00113803 0.269 N00006619 H1130 24K  D 2500 NV -0.024693 Republican rep  s22-109  12 support  2 
3 2006 S8NV00073 0.496 C00249342 0.421 N00006619 H1130 24K  D 5000 NV -0.024693 Republican rep  s22-109  12 support  2 
4 2006 S8NV00073 0.496 C00255752 0.254 N00006619 H1130 24K  D 4000 NV -0.024693 Republican rep  s22-109  12 support  2 

ich es so manipulieren möchten, dass die date Spalte ein Index, die feccandid Werte die Spaltenüberschriften sind (ich mache sie später ein zweiter Index, damit ich den Rahmen zum Panel schicken kann) und die anderen Spaltenköpfe werden Reihen. Gewünschte Ausgabe würde aussehen etwas wie folgt aus:

date feccandid    S8NV00072 S8NV00074 S8NV00075 S8NV00076 S8NV00077 
2006 feccandcfscore.dyn  0.496  0.496  0.496  0.496  0.496 
2006 pacid     C00000422 C00375360 C00113803 C00249342 C00255752 
2006 paccfscore     0.33  0.176  0.269   0.421 0.254 
2006 cid N00006619   N00006619 N00006619 N00006619 N00006619 
2006 catcode     H1100  H1100   H1130 H1130  H1130 
2006 type_x     24K   24K   24K 24K  24K 
2006 di       D   D    D  D  D 
2006 amtsum      5000  4500   2500  5000  4000 
2006 state      NV  NV   NV  NV   NV 
2006 log_diff_unemployment -0.024693 -0.024693 -0.024693 -0.024693 -0.024693 
2006 party      Republican Republican Republican Republican Republican 
2006 type_y       rep   rep   rep  rep  rep 
2006 bills       s22-109  s22-109 s22-109 s22-109  s22-109 
2006 years_exp        12  12  12  12  12 
2006 disposition      support  support support support support 
2006 billsum       3    3  2  2  2 

Ich habe versucht, die folgenden, wie durch jezrael empfohlen

zz=zz.pivot_table(index='date', columns='feccandid', aggfunc=np.mean) 

zz.head() 

    feccandcfscore.dyn ...  billsum 
feccandid H0AL02087 H0AL07060 H0AR01083 H0AR02107 H0AR03055 H0AR04038 H0AZ01259 H0AZ03362 H0CA15148 H0CA19173 ...  S8MI00158 S8MN00438 S8MS00055 S8MT00010 S8NC00239 S8NE00117 S8NM00010 S8NV00073 S8OR00207 S8WI00026 
date                      
2005 NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN 
2006 NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  2.125 NaN  NaN 
2007 NaN  0.016 NaN  NaN  NaN  -0.151 NaN  NaN  -0.777 NaN  ...  1.000000 NaN  1.666667 1.552632 NaN  NaN  2.0  1.000 NaN  2.0 
2008 NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  1.285714 NaN  NaN  5.431373 NaN  NaN  NaN  NaN  NaN  NaN 
2009 NaN  NaN  NaN  NaN  NaN  -0.086 NaN  NaN  -0.790 NaN  ...  NaN  NaN  NaN  2.433333 NaN  NaN  NaN  NaN  3.0  2.8 

Dies ist etwas in der Nähe zu dem, was Ich mag würde, außer, dass ich versuche um die feccandid als die einzigen Spaltenüberschriften und die ursprünglichen Spaltenüberschriften (die in diesem letzten Beispiel als die obersten Spaltenüberschriften sind) als Zeilen zu transponieren.

Answer 1

Ich glaube, Sie pivot_table verwenden können (Standard-Aggregatfunktion np.mean ist):

df = zz.pivot_table(index='date', columns='feccandid', fill_value='0', aggfunc=np.mean) 
df.columns = ['_'.join(col) for col in df.columns.values] 
print df 

Wenn Sie ersetzen müssen NaN-0:

print zz.pivot_table(index='date', columns='feccandid', fill_value='0', aggfunc=np.mean) 

EDIT:

ich kleine Probe erstellt DataFrame Wie ptrj sagt, können Sieverwendenund to_panel zum Erstellen panel. Dann brauchst du vielleicht transpose:

import pandas as pd 

zz = pd.DataFrame({'date': {0: 2001, 1: 2001, 2: 2002, 3: 2002}, 
        'feccandid': {0: 'S8NV00072', 1: 'S8NV00074', 
           2: 'S8NV00072', 3: 'S8NV00074'}, 
        'pacid': {0: 0.3, 1: 0.1, 2: 0.7, 3: 0.4}, 
        'billsum': {0: 1, 1: 2, 2: 5, 3: 6}}) 

print zz 
    billsum date feccandid pacid 
0  1 2001 S8NV00072 0.3 
1  2 2001 S8NV00074 0.1 
2  5 2002 S8NV00072 0.7 
3  6 2002 S8NV00074 0.4 

zz = zz.pivot_table(index='date', 
         columns='feccandid', 
         fill_value=0, 
         aggfunc=np.mean) 
print zz.T 
date    2001 2002 
     feccandid    
billsum S8NV00072 1.0 5.0 
     S8NV00074 2.0 6.0 
pacid S8NV00072 0.3 0.7 
     S8NV00074 0.1 0.4 

wp = zz.T.to_panel() 
print wp 
<class 'pandas.core.panel.Panel'> 
Dimensions: 2 (items) x 2 (major_axis) x 2 (minor_axis) 
Items axis: 2001 to 2002 
Major_axis axis: billsum to pacid 
Minor_axis axis: S8NV00072 to S8NV00074 

print wp.transpose(2, 0, 1) 

<class 'pandas.core.panel.Panel'> 
Dimensions: 2 (items) x 2 (major_axis) x 2 (minor_axis) 
Items axis: S8NV00072 to S8NV00074 
Major_axis axis: 2001 to 2002 
Minor_axis axis: billsum to pacid