我正在嘗試為數據框上的以下轉換獲取合理的解決方案：

給定此數據框：

生產：

即將多索引的分組級別填充到標準長度(行數)

在相當大的多索引數據幀(?幾千列和幾百萬行)上,是否有一種合理的快速方法？

這是給定的數據框字典,以供快速參考：

d = {'region': {0: 'intro',1: 'intro',2: 'intro',3: 'mid',4: 'mid',5: 'start',6: 'start',7: 'start',8: 'title',9: 'title'},'feat_index': {0: 9,1: 3,2: 0,3: 7,4: 8,5: 2,6: 4,7: 1,8: 6,9: 5},'position_in_region': {0: 422,1: 5834,2: 8813,3: 3187,4: 9407,5: 997,6: 3154,7: 8416,8: 5408,9: 8421},'document_0': {0: 0.39,1: 0.79,2: 0.01,3: 0.55,4: 0.99,5: 0.67,6: 0.61,7: 0.84,8: 0.15,9: 0.23},'document_1': {0: 0.8,1: 0.06,2: 0.92,3: 0.74,4: 0.06,5: 0.96,6: 0.57,7: 0.19,8: 0.29,9: 0.24},'document_2': {0: 0.81,1: 0.15,2: 0.19,3: 0.17,4: 0.11,5: 0.34,6: 0.8,7: 0.03,8: 0.67,9: 0.46}}

df = pd.DataFrame(d).set_index(['region','feat_index','position_in_region'])

最佳答案

@H_301_26@您可以使用由numpy.repeat和numpy.tile創建的幫助器DataFrame與左聯接合并：

#get number of new rows by Counter.most_common(1)

from collections import Counter

no_vals = Counter(df.index.labels[0]).most_common(1)[0][1]

print(no_vals)

3

df1 = pd.DataFrame({'region':np.repeat(df.index.levels[0],no_vals),'id': np.tile(np.arange(no_vals),len(np.unique(df.index.labels[0])))})

print (df1)

region id

0 intro 0

1 intro 1

2 intro 2

3 mid 0

4 mid 1

5 mid 2

6 start 0

7 start 1

8 start 2

9 title 0

10 title 1

11 title 2

#MultiIndex to columns

df = df.reset_index()

#new could with counter of regions

df.insert(1,'id',df.groupby('region').cumcount())

#merge,remove helper id columns and create MultiIndex

df = (df1.merge(df,how='left')

.drop('id',1)

.set_index(['region','position_in_region']))

print (df)

document_0 document_1 document_2

region feat_index position_in_region

intro 9.0 422.0 0.39 0.80 0.81

3.0 5834.0 0.79 0.06 0.15

0.0 8813.0 0.01 0.92 0.19

mid 7.0 3187.0 0.55 0.74 0.17

8.0 9407.0 0.99 0.06 0.11

NaN NaN NaN NaN NaN

start 2.0 997.0 0.67 0.96 0.34

4.0 3154.0 0.61 0.57 0.80

1.0 8416.0 0.84 0.19 0.03

title 6.0 5408.0 0.15 0.29 0.67

5.0 8421.0 0.23 0.24 0.46

NaN NaN NaN NaN NaN

from collections import Counter

no_vals = Counter(df.index.labels[0]).most_common(1)[0][1]

print(no_vals)

3

mux = pd.MultiIndex.from_product([df.index.levels[0],np.arange(no_vals)],names=['region','id'])

print (mux)

MultiIndex(levels=[['intro','mid','start','title'],[0,1,2]],codes=[[0,2,3,3],'id'])

df = df.reset_index(level=[1,2]).set_index(df.groupby(level=0).cumcount(),append=True)

df = (df.reindex(mux).reset_index(level=1,drop=True)

.set_index(['feat_index','position_in_region'],append=True))

print (df)

document_0 document_1 document_2

region feat_index position_in_region

intro 9.0 422.0 0.39 0.80 0.81

3.0 5834.0 0.79 0.06 0.15

0.0 8813.0 0.01 0.92 0.19

mid 7.0 3187.0 0.55 0.74 0.17

8.0 9407.0 0.99 0.06 0.11

NaN NaN NaN NaN NaN

start 2.0 997.0 0.67 0.96 0.34

4.0 3154.0 0.61 0.57 0.80

1.0 8416.0 0.84 0.19 0.03

title 6.0 5408.0 0.15 0.29 0.67

5.0 8421.0 0.23 0.24 0.46

NaN NaN NaN NaN NaN

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