Question

我无法通过从右表中进行采样来找到一种很好的熊猫式方法来填充缺失的NaN值以进行左连接。

例如 join_left = left.merge（right，how =“ left”，left_on = [attr1]，right_on = [attr2]）从左和右

产生类似

   0  1_x  2_x  1_y  2_y
0  1    1    1  2.0  2.0
1  1    1    1  2.0  3.0
2  2    2    2  NaN  NaN
3  3    3    3  2.0  2.0
4  3    3    3  2.0  9.0
5  3    3    3  2.0  2.0
6  9    9    9  NaN  NaN
7  1    3    2  2.0  2.0
8  1    3    2  2.0  3.0

如何从右表中采样一行而不是填充NaN？

这是我到目前为止尝试过的playground：

left = [[1,1,1], [2,2,2],[3,3,3], [9,9,9], [1,3,2]]
right = [[1,2,2],[1,2,3],[3,2,2], [3,2,9], [3,2,2]]
left = np.asarray(left)
right = np.asarray(right)
left = pd.DataFrame(left)
right = pd.DataFrame(right)
joined_left = left.merge(right, how="left", left_on=[0], right_on=[0])

while(joined_left.isnull().values.any()):
    right_sample = right.sample().drop(0, axis=1)
    joined_left.fillna(value=right_sample, limit=1)

print joined_left

基本上随机抽样，并使用fillna（）首次出现NaN值进行填充...但是由于某些原因，我没有输出。

谢谢！

其中一个输出可能是

   0  1_x  2_x  1_y  2_y
0  1    1    1  2.0  2.0
1  1    1    1  2.0  3.0
2  2    2    2  2.0  2.0
3  3    3    3  2.0  2.0
4  3    3    3  2.0  9.0
5  3    3    3  2.0  2.0
6  9    9    9  3.0  2.9
7  1    3    2  2.0  2.0
8  1    3    2  2.0  3.0

带有采样的3 2 2和3 2 9

Answer 1

将sample与fillna一起使用

joined_left = left.merge(right, how="left", left_on=[0], right_on=[0],indicator=True) # adding indicator
joined_left
Out[705]: 
   0  1_x  2_x  1_y  2_y     _merge
0  1    1    1  2.0  2.0       both
1  1    1    1  2.0  3.0       both
2  2    2    2  NaN  NaN  left_only
3  3    3    3  2.0  2.0       both
4  3    3    3  2.0  9.0       both
5  3    3    3  2.0  2.0       both
6  9    9    9  NaN  NaN  left_only
7  1    3    2  2.0  2.0       both
8  1    3    2  2.0  3.0       both
nnull=joined_left['_merge'].eq('left_only').sum() # find all many row miss match , at the mergedf
s=right.sample(nnull)# rasmple from the dataframe after dropna 
s.index=joined_left.index[joined_left['_merge'].eq('left_only')] # reset the index of the subset fill df to the index of null value show up 
joined_left.fillna(s.rename(columns={1:'1_y',2:'2_y'})) 
Out[706]: 
   0  1_x  2_x  1_y  2_y     _merge
0  1    1    1  2.0  2.0       both
1  1    1    1  2.0  3.0       both
2  2    2    2  2.0  2.0  left_only
3  3    3    3  2.0  2.0       both
4  3    3    3  2.0  9.0       both
5  3    3    3  2.0  2.0       both
6  9    9    9  2.0  3.0  left_only
7  1    3    2  2.0  2.0       both
8  1    3    2  2.0  3.0       both

通过从右表中进行抽样，为左联接填写NaN值

1 个答案: