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python - 通过从右表中采样填充左连接的 NaN 值

问题描述

我想不出一种很好的熊猫方式来通过从右表中采样来填充左连接的缺失 NaN 值。

例如，joined_left = left.merge(right, how="left", left_on=[attr1], right_on=[attr2]) 从左到右

   0  1  2
0  1  1  1
1  2  2  2
2  3  3  3
3  9  9  9
4  1  3  2

   0  1  2
0  1  2  2
1  1  2  3
2  3  2  2
3  3  2  9
4  3  2  2

产生类似的东西

   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操场：

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

基本上随机采样并使用 fillna() 来填充 NaN 值的第一次出现......但由于某种原因我没有得到任何输出。

谢谢！

输出之一可能是 

   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

与采样3 2 2和3 2 9

标签： pythonpandas

解决方案

与sample_fillna 

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

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