当索引和列都是多索引时重置索引

问题描述

我有一个在行和列中都有多索引的 df，我想在索引和列上都重置索引，以便所有多索引都是新列.以下是我拥有和想要的示例.

I have a df with multi index in both rows and cols, and I want to reset_index on both index and cols so that all the mulitindices are new columns. Below is an example of what I have and what I want.

我有什么:

indexAarrays = [['bar', 'bar', 'baz', 'baz', ],
          ['one', 'two', 'one', 'two']]
indexTuples = list(zip(*indexAarrays))
index =  pd.MultiIndex.from_tuples(indexTuples, names=['firstIndex', 'secondIndex'])

colAarrays = [['c1', 'c1', 'c2', 'c2', ],
          ['d1', 'd2', 'd1', 'd2']]
colTuples = list(zip(*colAarrays ))
col =  pd.MultiIndex.from_tuples(colTuples, names=['firstCol', 'secondCol'])

df = pd.DataFrame(data=np.random.random_sample((len(index), len(col))),
                  index=index, columns=col)
df

以上给出了我拥有的 DF:

The above gives the DF i have:

firstCol                      c1                  c2
secondCol                     d1        d2        d1        d2
firstIndex secondIndex
bar        one          0.231221  0.846196  0.037493  0.516474
           two          0.810847  0.204095  0.423766  0.513262
baz        one          0.433040  0.118018  0.267039  0.356261
           two          0.529042  0.181886  0.093488  0.643357

我想要的:

wantedCols = [idxName for idxName in index.names] \
                        + [colName for colName in col.names]\
                        + ['Value']
dfWanted = pd.DataFrame(index = range(int(df.shape[0]*df.shape[1]/(len(wantedCols)-1))),
                        columns=wantedCols)

idxCounter = 0
for idx1 in df.index.get_level_values(0).unique():
    for idx2 in df.index.get_level_values(1).unique():
        for c1 in df.columns.get_level_values(0).unique():
            for c2 in df.columns.get_level_values(1).unique():
                dfWanted.loc[idxCounter, 'firstIndex'] = idx1
                dfWanted.loc[idxCounter, 'secondIndex'] = idx2
                dfWanted.loc[idxCounter, 'firstCol'] = c1
                dfWanted.loc[idxCounter, 'secondCol'] = c2
                dfWanted.loc[idxCounter, 'Value'] = df.loc[(idx1, idx2), (c1, c2)]
                idxCounter += 1

dfWanted

上面给出了我想要的 DF:

The above gives the DF I want:

firstIndex secondIndex firstCol secondCol      Value
0         bar         one       c1        d1   0.231221
1         bar         one       c1        d2   0.846196
2         bar         one       c2        d1   0.037493
3         bar         one       c2        d2   0.516474
4         bar         two       c1        d1   0.810847
5         bar         two       c1        d2   0.204095
6         bar         two       c2        d1   0.423766
7         bar         two       c2        d2   0.513262
8         baz         one       c1        d1    0.43304
9         baz         one       c1        d2   0.118018
10        baz         one       c2        d1   0.267039
11        baz         one       c2        d2   0.356261
12        baz         two       c1        d1   0.529042
13        baz         two       c1        d2   0.181886
14        baz         two       c2        d1  0.0934878
15        baz         two       c2        d2   0.643357

有没有人知道比我上面使用的方法更简单的重置索引的方法?

Does anyone know of an easier way to reset the indices than the method I used above?

推荐答案

使用 DataFrame.stack 按两个级别，然后通过 :

Use DataFrame.stack by both levels and then convert MultiIndex Series to columns by Series.reset_index:

df = df.stack([0,1]).reset_index(name='Value')
print (df)
   firstIndex secondIndex firstCol secondCol     Value
0         bar         one       c1        d1  0.746027
1         bar         one       c1        d2  0.622784
2         bar         one       c2        d1  0.613197
3         bar         one       c2        d2  0.449560
4         bar         two       c1        d1  0.560810
5         bar         two       c1        d2  0.125046
6         bar         two       c2        d1  0.147148
7         bar         two       c2        d2  0.622862
8         baz         one       c1        d1  0.537280
9         baz         one       c1        d2  0.801410
10        baz         one       c2        d1  0.889445
11        baz         one       c2        d2  0.226477
12        baz         two       c1        d1  0.100759
13        baz         two       c1        d2  0.279383
14        baz         two       c2        d1  0.041767
15        baz         two       c2        d2  0.739942

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