南音pd.数据帧（simmetric矩阵）

Question

我有一个这样的数据帧。我想把南区移走，把牢房移上去。然后添加一个日期列并将其设置为索引。你知道吗

                ciao      google    microsoft
Search Volume   368000    NaN       NaN
Search Volume   368000    NaN       NaN
Search Volume   450000    NaN       NaN
Search Volume   450000    NaN       NaN
Search Volume   450000    NaN       NaN
Search Volume   450000    NaN       NaN
Search Volume   NaN       37200000  NaN
Search Volume   NaN       37200000  NaN
Search Volume   NaN       37200000  NaN
Search Volume   NaN       37200000  NaN
Search Volume   NaN       37200000  NaN
Search Volume   NaN       37200000  NaN
Search Volume   NaN       NaN       135000
Search Volume   NaN       NaN       135000
Search Volume   NaN       NaN       110000
Search Volume   NaN       NaN       110000
Search Volume   NaN       NaN       110000
Search Volume   NaN       NaN       110000

输出应如下所示：

date = ['20140115', '20140215', '20140315', '20140415', '20140515', '20140615']

date        ciao    google      microsoft
20140115    368000  37200000    135000
20140215    368000  37200000    135000
20140315    450000  37200000    110000
20140415    450000  37200000    110000
20140515    450000  37200000    110000
20140615    450000  37200000    110000

看起来很简单，但我不知道怎么做。谢谢

Answer 1

您可以使用dropna：

df = df.apply(lambda x: pd.Series(x.dropna().values)).fillna('')
df['date'] = date
print(df)

输出：

     ciao      google   microsoft  date     
 368000.0  37200000.0   135000.0   20140115 
 368000.0  37200000.0   135000.0   20140215 
 450000.0  37200000.0   110000.0   20140315 
 450000.0  37200000.0   110000.0   20140415 
 450000.0  37200000.0   110000.0   20140515 
 450000.0  37200000.0   110000.0   20140615 

Answer 2

也可以在列上使用dropna作为序列

df1=pd.DataFrame(data=[df[i].dropna().values for i in df.columns]).T
df1.index=dates

Answer 3

一个棘手的解决办法，因为你有重复的索引

pd.concat([df[x].dropna() for x in df.columns],1)
Out[24]: 
                  ciao      google  microsoft
SearchVolume  368000.0  37200000.0   135000.0
SearchVolume  368000.0  37200000.0   135000.0
SearchVolume  450000.0  37200000.0   110000.0
SearchVolume  450000.0  37200000.0   110000.0
SearchVolume  450000.0  37200000.0   110000.0
SearchVolume  450000.0  37200000.0   110000.0