我嘗試將多個(gè)新的dataFrames合并到一個(gè)主框架中。假設(shè)主數(shù)據(jù)框： key1 key20 0.365803 0.2591121 0.086869 0.5898342 0.269619 0.1836443 0.755826 0.0451874 0.204009 0.669371我嘗試將以下兩個(gè)數(shù)據(jù)集合并到主要數(shù)據(jù)集“新數(shù)據(jù)1 ”中： key1 key2 new feature0 0.365803 0.259112 info1新數(shù)據(jù)2： key1 key2 new feature0 0.204009 0.669371 info2預(yù)期結(jié)果： key1 key2 new feature0 0.365803 0.259112 info11 0.776945 0.780978 NaN2 0.275891 0.114998 NaN3 0.667057 0.373029 NaN4 0.204009 0.669371 info2我試過(guò)的test = test.merge(data1, left_on=['key1', 'key2'], right_on=['key1', 'key2'], how='left')test = test.merge(data2, left_on=['key1', 'key2'], right_on=['key1', 'key2'], how='left')第一個(gè)效果很好，但第二個(gè)效果不好，我得到的結(jié)果是： key1 key2 new feature_x new feature_y0 0.365803 0.259112 info1 NaN1 0.776945 0.780978 NaN NaN2 0.275891 0.114998 NaN NaN3 0.667057 0.373029 NaN NaN4 0.204009 0.669371 NaN info2謝謝你的幫助！
3 回答
aluckdog
TA貢獻(xiàn)1847條經(jīng)驗(yàn) 獲得超7個(gè)贊
首先append或concat兩者DataFrame在一起，然后merge：
dat = pd.concat([data1, data2], ignore_index=True)
或者：
dat = data1.append(data2, ignore_index=True)
print (dat)
key1 key2 new feature
0 0.365803 0.259112 info1
1 0.204009 0.669371 info2
#if same joined columns names better is only on parameter
df = test.merge(dat, on=['key1', 'key2'], how='left')
print (df)
key1 key2 new feature
0 0.365803 0.259112 info1
1 0.086869 0.589834 NaN
2 0.269619 0.183644 NaN
3 0.755826 0.045187 NaN
4 0.204009 0.669371 info2
胡說(shuō)叔叔
TA貢獻(xiàn)1804條經(jīng)驗(yàn) 獲得超8個(gè)贊
您可以pd.DataFrame.update改用：
# create new column and set index
res = test.assign(newfeature=None).set_index(['key1', 'key2'])
# update with new data sequentially
res.update(data1.set_index(['key1', 'key2']))
res.update(data2.set_index(['key1', 'key2']))
# reset index to recover columns
res = res.reset_index()
print(res)
key1 key2 newfeature
0 0.365803 0.259112 info1
1 0.086869 0.589834 None
2 0.269619 0.183644 None
3 0.755826 0.045187 None
4 0.204009 0.669371 info2
哈士奇WWW
TA貢獻(xiàn)1799條經(jīng)驗(yàn) 獲得超6個(gè)贊
您還可以將數(shù)據(jù)幀設(shè)置為相同的索引并使用簡(jiǎn)單 loc
df = df.set_index(["key1", "key2"])
df2 = df2.set_index(["key1", "key2"])
然后
df.loc[:, "new_feature"] = df2['new_feature']
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