Question

我有一个大型的pandas数据框，包含8列和几个NaN值：

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

我怎样才能像这样压扁它：

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)

我读了docs并尝试了：

df.iloc[:,0]

问题是我丢失了其他列的信息和订单。我想到如何平坦而不会丢失其他单元格和顺序中的数据？

Answer 1

您可以堆叠列并选择性地重置索引。默认情况下，堆栈会丢弃NaN＆＃39。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

这可能就是诀窍：

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

输出：

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

P.S。请查看this question关于在问题中提供好的熊猫示例。

哪种是放宽熊猫数据帧的最有效方法？

2 个答案: