python - 提取子DataFrame

Question

我在 Pandas 中有这种数据框:

NaN
1
NaN
452
1175
12
NaN
NaN
NaN
145
125
NaN
1259
2178
2514
1

另一方面，我有另一个数据框:

1
2
3
4
5
6

我想将第一个数据帧分成不同的子数据帧，如下所示:

DataFrame 1:
  1
DataFrame 2:
  452
  1175
  12
DataFrame 3:

DataFrame 4:

DataFrame 5:
  145
  125
DataFrame 6:
  1259
  2178
  2514
  1

如何在没有循环的情况下做到这一点？

Answer 1

更新:感谢@piRSquared指出上述解决方案不适用于具有非数字索引的 DF/Series。这是更通用的解决方案:

dfs = [x.dropna()
       for x in np.split(df, np.arange(len(df))[df['column'].isnull().values])]

旧答案:

IIUC 你可以这样做:

来源 DF:

In [40]: df
Out[40]:
    column
0      NaN
1      1.0
2      NaN
3    452.0
4   1175.0
5     12.0
6      NaN
7      NaN
8      NaN
9    145.0
10   125.0
11     NaN
12  1259.0
13  2178.0
14  2514.0
15     1.0

解决方案:

In [31]: dfs = [x.dropna()
                for x in np.split(df, df.index[df['column'].isnull()].values+1)]

In [32]: dfs[0]
Out[32]:
Empty DataFrame
Columns: [column]
Index: []

In [33]: dfs[1]
Out[33]:
   column
1     1.0

In [34]: dfs[2]
Out[34]:
   column
3   452.0
4  1175.0
5    12.0

In [35]: dfs[3]
Out[35]:
Empty DataFrame
Columns: [column]
Index: []

In [36]: dfs[4]
Out[36]:
Empty DataFrame
Columns: [column]
Index: []

In [37]: dfs[4]
Out[37]:
Empty DataFrame
Columns: [column]
Index: []

In [38]: dfs[5]
Out[38]:
    column
9    145.0
10   125.0

In [39]: dfs[6]
Out[39]:
    column
12  1259.0
13  2178.0
14  2514.0
15     1.0