階層型インデックス付きパンダデータフレームに空白行を追加

Question

もっと便利に表示するために、下のDataFrameの各レベル0行ラベルの後に空白行を配置しました。私は、私が必要としなかったそれぞれの価値の行を持っていたので、それを変えました。

私のハックの仕事とは対照的に、それについて正しい正しい方法は何ですか？

       B      S 
         Depth3 Depth2 Depth1 Depth1 Depth2 Depth3 
Market1 actionIndicator  B  B  B  S  S  S 
     limit    555 557 558  560  561  562 
     quantity   18.474 8.355 8.286 0.626 17.101 17.576 
Market2 actionIndicator  B  B  B  S  S  S 
     limit    475 477 478  479  480  481 
     quantity   30.156 8.329 8.182 37.483 84.416 37.659 
Market3 actionIndicator  B  B  B  S  S  S 
     limit    583 586 587  589  590  591 
     quantity   39.548 8.382 8.18 55.181 17.289 17.689 

私はこれ欲しかっ：以下は、それを達成するために、元のデータフレームで使用されていた

     B      S 
        Depth3 Depth2 Depth1 Depth1 Depth2 Depth3 
Market1 limit  555 557 558  560  561  562 
     quantity 18.474 8.355 8.286 0.626 17.101 17.576 

Market2 limit  475 477 478  479  480  481 
     quantity 30.156 8.329 8.182 37.483 84.416 37.659 

Market3 limit  583 586 587  589  590  591 
     quantity 39.548 8.382 8.18 55.181 17.289 17.689 

を：

df = df.reindex(display_order, level=0).reindex(["limit", "quantity", 
               "actionIndicator"], level=1) 
df.index = df.index.set_levels(["limit", "quantity", ""], level=1) 
for level in set(df.index.get_level_values(0)): 
    df.ix[(level, "")] = "" 

全コード：あなたが使用することができます

data = { 
'Market1': {'B': {'Depth1': {'actionIndicator': 'B', 
    'limit': '558', 
    'quantity': '8.286'}, 
    'Depth2': {'actionIndicator': 'B', 'limit': '557', 'quantity': '8.355'}, 
    'Depth3': {'actionIndicator': 'B', 'limit': '555', 'quantity': '18.474'}}, 
    'S': {'Depth1': {'actionIndicator': 'S', 
    'limit': '560', 
    'quantity': '0.626'}, 
    'Depth2': {'actionIndicator': 'S', 'limit': '561', 'quantity': '17.101'}, 
    'Depth3': {'actionIndicator': 'S', 'limit': '562', 'quantity': '17.576'}}}, 
'Market2': {'B': {'Depth1': {'actionIndicator': 'B', 
    'limit': '478', 
    'quantity': '8.182'}, 
    'Depth2': {'actionIndicator': 'B', 'limit': '477', 'quantity': '8.329'}, 
    'Depth3': {'actionIndicator': 'B', 'limit': '475', 'quantity': '30.156'}}, 
    'S': {'Depth1': {'actionIndicator': 'S', 
    'limit': '479', 
    'quantity': '37.483'}, 
    'Depth2': {'actionIndicator': 'S', 'limit': '480', 'quantity': '84.416'}, 
    'Depth3': {'actionIndicator': 'S', 'limit': '481', 'quantity': '37.659'}}}, 
'Market3': {'B': {'Depth1': {'actionIndicator': 'B', 
    'limit': '587', 
    'quantity': '8.18'}, 
    'Depth2': {'actionIndicator': 'B', 'limit': '586', 'quantity': '8.382'}, 
    'Depth3': {'actionIndicator': 'B', 'limit': '583', 'quantity': '39.548'}}, 
    'S': {'Depth1': {'actionIndicator': 'S', 
    'limit': '589', 
    'quantity': '55.181'}, 
    'Depth2': {'actionIndicator': 'S', 'limit': '590', 'quantity': '17.289'}, 
    'Depth3': {'actionIndicator': 'S', 'limit': '591', 'quantity': '17.689'}}}, 
} 

df = DataFrame.from_dict(
     {(k1, k2, k3): data[k1][k2][k3] for k1 in data.keys() for k2 in 
     data[k1].keys() for k3 in data[k1][k2].keys()}, orient="columns") 

df = df.unstack().reorder_levels([0,3,2,1]).unstack(level=[3,2]) 
dfB = df[["B"]].sort_index(axis=1, ascending=False) 
dfS = df[["S"]] 
df = pd.concat([dfB, dfS], axis=1) 

df = df.reindex(["limit", "quantity", "actionIndicator"], level=1) 
df.index = df.index.set_levels(["limit", "quantity", ""], level=1) 
for level in set(df.index.get_level_values(0)): 
    df.ix[(level, "")] = "" 

print(df) 

Answer 1

別のハッキング：

df = pd.DataFrame.from_dict(
     {(k1, k2, k3): data[k1][k2][k3] for k1 in data.keys() for k2 in 
     data[k1].keys() for k3 in data[k1][k2].keys()}, orient="columns") 

df = df.stack(0).swaplevel(1,0).sort_index() 
print (df) 
          B      S     
         Depth1 Depth2 Depth3 Depth1 Depth2 Depth3 
Market1 actionIndicator  B  B  B  S  S  S 
     limit    558 557  555  560  561  562 
     quantity   8.286 8.355 18.474 0.626 17.101 17.576 
Market2 actionIndicator  B  B  B  S  S  S 
     limit    478 477  475  479  480  481 
     quantity   8.182 8.329 30.156 37.483 84.416 37.659 
Market3 actionIndicator  B  B  B  S  S  S 
     limit    587 586  583  589  590  591 
     quantity   8.18 8.382 39.548 55.181 17.289 17.689 

#rename index value 'actionIndicator' for last for sort e.g. zzz 
df = df.rename(index={'actionIndicator':'zzz'}) 
#sorting index, zzz row is last in each group 
df = df.sort_index() 
#get mask where zzz 
mask = df.index.get_level_values(1) == 'zzz' 
#replace rows where mask is True by empty spaces 
df.loc[mask] = '' 
#replace value zzz to empty space 
df = df.rename(index={'zzz':''}) 
print (df) 
         B      S     
       Depth1 Depth2 Depth3 Depth1 Depth2 Depth3 
Market1 limit  558 557  555  560  561  562 
     quantity 8.286 8.355 18.474 0.626 17.101 17.576 

Market2 limit  478 477  475  479  480  481 
     quantity 8.182 8.329 30.156 37.483 84.416 37.659 

Market3 limit  587 586  583  589  590  591 
     quantity 8.18 8.382 39.548 55.181 17.289 17.689 

MultiIndex.from_productによってreindexともう1つの可能な解決策：

#dynamically remove 'actionIndicator' and add it to the end of list 
a = 'actionIndicator' 
lvls = [lvl for lvl in df.index.levels[1] if lvl != a] + [a] 
print (lvls) 
['limit', 'quantity', 'actionIndicator'] 

df = df.reindex(pd.MultiIndex.from_product([df.index.levels[0],lvls])) 
mask = df.index.get_level_values(1) == a 
df.loc[mask] = '' 
df = df.rename(index={a:''}) 
print (df) 
         B      S     
       Depth1 Depth2 Depth3 Depth1 Depth2 Depth3 
Market1 limit  558 557  555  560  561  562 
     quantity 8.286 8.355 18.474 0.626 17.101 17.576 

Market2 limit  478 477  475  479  480  481 
     quantity 8.182 8.329 30.156 37.483 84.416 37.659 

Market3 limit  587 586  583  589  590  591 
     quantity 8.18 8.382 39.548 55.181 17.289 17.689