哪种是放宽熊猫数据帧的最有效方法？

Question

I have a large pandas dataframe with 8 columns and several NaN values:

0   1   2   3   4   5   6   7   8
1   Google, Inc. (Date 11/07/2016)  NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
2   Apple Inc. (Date 07/01/2016)    Amazon (Date 11/01/2016)    NaN     NaN     NaN     NaN     NaN     NaN     NaN
3   IBM, Inc. (Date 11/08/2016)     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN
4   Microsoft (Date 11/10/2016)     Google, Inc. (Date 11/10/1990)  Google, Inc. (Date 11/07/2016)  Samsung (Date 05/02/2016)   NaN     NaN     NaN     NaN     NaN

How can I flatten down it like this:

0   companies
1   Google, Inc. (Date 11/07/2016)
2   Apple Inc. (Date 07/01/2016)
3   Amazon (Date 11/01/2016)
4   IBM, Inc. (Date 11/08/2016)
5   Microsoft (Date 11/10/2016)
6   Google, Inc. (Date 11/10/1990)
7   Google, Inc. (Date 11/07/2016)
8   Samsung (Date 05/02/2016)

I read the docs and tried:

df.iloc[:,0]

The problem is that I lost information and order over the other columns. I idea of how to flat without lost data in the other cells and order?.

Answer 1

You can stack the columns and optionally reset the index. By default, stack drops NaN's.

df.stack()
Out: 
0  0    Google, Inc. (Date 11/07/2016) 
1  0      Apple Inc. (Date 07/01/2016) 
   1          Amazon (Date 11/01/2016) 
2  0       IBM, Inc. (Date 11/08/2016) 
3  0       Microsoft (Date 11/10/2016) 
   1    Google, Inc. (Date 11/10/1990) 
   2    Google, Inc. (Date 11/07/2016) 
   3         Samsung (Date 05/02/2016) 
dtype: object

df.stack().reset_index(drop=True)
Out: 
0    Google, Inc. (Date 11/07/2016) 
1      Apple Inc. (Date 07/01/2016) 
2          Amazon (Date 11/01/2016) 
3       IBM, Inc. (Date 11/08/2016) 
4       Microsoft (Date 11/10/2016) 
5    Google, Inc. (Date 11/10/1990) 
6    Google, Inc. (Date 11/07/2016) 
7         Samsung (Date 05/02/2016) 
dtype: object

Answer 2

This probably do the trick:

df = pd.DataFrame([
        ["Google, Inc. (Date 11/07/2016)", float("NaN")], 
        ["Apple Inc. (Date 07/01/2016)", "Amazon (Date 11/01/2016)"]])
unstacked = df.T.unstack()
unstacked.dropna(inplace=True)
unstacked.reset_index(drop=True, inplace=True)
unstacked

Output:

0    Google, Inc. (Date 11/07/2016)
1      Apple Inc. (Date 07/01/2016)
2          Amazon (Date 11/01/2016)
dtype: object

PS Please, take a look at this question on providing good pandas examples in questions.