我無法找到一種很好的熊貓式方法來通過從右表中采樣來填充左連接缺失的 NaN 值。例如joined_left = left.merge(right, how="left", left_on=[attr1], right_on=[attr2]) 從左到右 0 1 20 1 1 11 2 2 22 3 3 33 9 9 94 1 3 2 0 1 20 1 2 21 1 2 32 3 2 23 3 2 94 3 2 2產(chǎn)生像 0 1_x 2_x 1_y 2_y0 1 1 1 2.0 2.01 1 1 1 2.0 3.02 2 2 2 NaN NaN3 3 3 3 2.0 2.04 3 3 3 2.0 9.05 3 3 3 2.0 2.06 9 9 9 NaN NaN7 1 3 2 2.0 2.08 1 3 2 2.0 3.0如何從右表中采樣一行而不是填充 NaN？這是我到目前為止嘗試過的操場：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基本上隨機(jī)采樣并使用 fillna() 首次出現(xiàn) NaN 值來填充......但由于某種原因我沒有得到任何輸出。謝謝！輸出之一可能是 0 1_x 2_x 1_y 2_y0 1 1 1 2.0 2.01 1 1 1 2.0 3.02 2 2 2 2.0 2.03 3 3 3 2.0 2.04 3 3 3 2.0 9.05 3 3 3 2.0 2.06 9 9 9 3.0 2.97 1 3 2 2.0 2.08 1 3 2 2.0 3.0與采樣3 2 2和3 2 9
1 回答
智慧大石
TA貢獻(xiàn)1946條經(jīng)驗 獲得超3個贊
使用sample與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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