如何在 pandas 数据框中将单元格的值拆分为多行? [英] How to split values of a cell in multiple rows in pandas data frame?

问题描述

我有一个下面的数据框，它是使用代码获得的:

I have a following data frame, which was obtained using the code:

     df1=df.groupby('id')['x,y'].apply(lambda x: rdp(x.tolist(), 5.0)).reset_index()

请在此处

获得的结果数据帧:

      id          x,y
  0   1    [(0, 0), (1, 2)]
  1   2    [(1, 3), (1, 2)]
  2   3    [(2, 5), (4, 6)]  

是否有可能得到这样的东西:

Is it possible to get something like this:

         id      x,y
     0   1      (0, 0)
     1   1      (1, 2)
     2   2      (1, 3)
     3   2      (1, 2)
     4   3      (2, 5)
     5   3      (4, 6)

在这里，作为上一个df结果获得的坐标列表将根据其各自的ID分成新的行.

Here, the list of coordinates obtained as a result in previous df is split into new rows against their respective ids.

推荐答案

您可以将DataFrame构造函数与

You can use DataFrame constructor with stack:

df2 = pd.DataFrame(df1['x,y'].values.tolist(), index=df1['id'])
        .stack()
        .reset_index(level=1, drop=True)
        .reset_index(name='x,y')
print (df2)

   id     x,y
0   1  (0, 0)
1   1  (1, 2)
2   2  (1, 3)
3   2  (1, 2)
4   3  (2, 5)
5   3  (4, 6)

numpy解决方案使用 numpy.repeat 通过lengths通过 ，x,y列由 numpy.ndarray.sum :

numpy solution use numpy.repeat by lengths of values by str.len, x,y column is flattenig by numpy.ndarray.sum:

df2 = pd.DataFrame({'id': np.repeat(df1['id'].values, df1['x,y'].str.len()), 
                   'x,y': df1['x,y'].values.sum()})

print (df2)
   id     x,y
0   1  (0, 0)
0   1  (1, 2)
1   2  (1, 3)
1   2  (1, 2)
2   3  (2, 5)
2   3  (1, 9)
2   3  (4, 6)

时间:

In [54]: %timeit pd.DataFrame(df1['x,y'].values.tolist(), index=df1['id']).stack().reset_index(level=1, drop=True).reset_index(name='x,y')
1000 loops, best of 3: 1.49 ms per loop

In [55]: %timeit pd.DataFrame({'id': np.repeat(df1['id'].values, df1['x,y'].str.len()), 'x,y': df1['x,y'].values.sum()})
1000 loops, best of 3: 562 µs per loop

#piRSquared solution
In [56]: %timeit pd.DataFrame({'id': df1['id'].repeat(df1['x,y'].str.len()), 'x,y': df1['x,y'].sum() })
1000 loops, best of 3: 712 µs per loop

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