我有一个df,
查询主题相同
0 WP_77.1 WP_706.1 HPS_1
1 WP_78.1 WP_46.1 HPS_2
2 WP_57.1 WP_26.1 HPS_3
3 WP_57.1 WP_627.1 HPS_4
4 WP_15.1 WP_16.1 HPS_5
5 WP_15.1 WP_17.1 HPS_6
6 WP_15.1 WP_63.1 HPS_7
7 WP_15.1 WP_61.1 HPS_8
8 WP_15.1 WP_56.1 HPS_9
9水电站40.1水电站11.1水电站10
我试过了
df['query_s'] = df['query'].shift(-1)
df['HPSame_s'] = df['HPSame'].shift(-1)
condition = [(df['query'] == df['query_s'])]
ifTrue = df['HPSame']
ifFalse = df['HPSame_s']
df['match'] = np.where(condition, ifTrue, ifFalse)
这会引发ValueError:值的长度与索引的长度不匹配
我也试过了,但没有达到预期效果
df.loc[(df['query'] == df['query_s']), 'match'] = df['HPSame']
df.loc[(df['query'] != df['query_s']), 'match'] = df['HPSame_s']
我正在寻找的结果是, df= 查询主题相同匹配 0 WP_77.1 WP_706.1 HPS_1 HPS_1 1 WP_78.1 WP_46.1 HPS_2 HPS_2 2 WP_57.1 WP_26.1 HPS_3 HPS_3 3 WP_57.1 WP_627.1水电站4水电站3 4 WP_15.1 WP_16.1 HPS_5 HPS_5 5水电站15.1水电站17.1水电站6水电站5 6 WP_15.1 WP_63.1水电站7水电站5 7水电站15.1水电站61.1水电站8水电站5 8水电站15.1水电站56.1水电站9水电站5 9 WP_40.1 WP_11.1 HPS_10 HPS_10
您可以使用
ffill
:输出:
您还可以使用
groupby.transform('first')
,如中所示输出
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