[英]How to group 2 dataframes based on different conditions and columns in pandas
我将在这里使用嵌套 。
df1['ListCol']=df1['ColC']# Here I am try to record the original data
Yourdf=unnesting(df1,['ColC']).merge(df2, on=['ColA','ColC'],how='inner')
Yourdf
ColC ColA ColB ListCol
0 2 A 1 [1, 2, 3]
1 3 A 1 [1, 2, 3]
2 6 A 2 [4, 5, 6]
3 2 B 4 [1, 2, 3]
4 5 B 5 [3, 4, 5]
def unnesting(df, explode):
idx = df.index.repeat(df[explode[0]].str.len())
df1 = pd.concat([
pd.DataFrame({x: np.concatenate(df[x].values)}) for x in explode], axis=1)
df1.index = idx
return df1.join(df.drop(explode, 1), how='left')
您可以这样进行,在数据帧一df1中扩展ColC,然后将ColA上的合并和df1中的“ melted”列融为一列:
df1 = pd.DataFrame({'ColA':[*'AABBB'],
'ColB':[1,2,3,4,5],
'ColC':[[1,2,3],[4,5,6],[7,8,9],[1,2,3],[3,4,5]]})
df2 = pd.DataFrame({'ColA':[*'AAABB'], 'ColC':[3,6,2,2,5]})
df1_m = df1.assign(**pd.DataFrame([i for i in df1['ColC'].values]).add_prefix('ColC_'))\
.melt(['ColA','ColB','ColC'])
df_out = df2.merge(df1_m, left_on=['ColA','ColC'], right_on=['ColA','value'])
df_out
输出:
ColA ColC_x ColB ColC_y variable value
0 A 3 1 [1, 2, 3] ColC_2 3
1 A 6 2 [4, 5, 6] ColC_2 6
2 A 2 1 [1, 2, 3] ColC_1 2
3 B 2 4 [1, 2, 3] ColC_1 2
4 B 5 5 [3, 4, 5] ColC_2 5
另一种方法是使用merge
的ColA
和apply
与蟒蛇in
运营商只挑行,其中ColC_y
是ColC_x
In [19]: df1
Out[19]:
ColA ColB ColC
0 A 1 [1, 2, 3]
1 A 2 [4, 5, 6]
2 B 3 [7, 8, 9]
3 B 4 [1, 2, 3]
4 B 5 [3, 4, 5]
In [20]: df2
Out[20]:
ColA ColC
0 A 3
1 A 6
2 A 2
3 B 2
4 B 5
In [21]: df3 = df1.merge(df2, on=['ColA'])
In [22]: df3
Out[22]:
ColA ColB ColC_x ColC_y
0 A 1 [1, 2, 3] 3
1 A 1 [1, 2, 3] 6
2 A 1 [1, 2, 3] 2
3 A 2 [4, 5, 6] 3
4 A 2 [4, 5, 6] 6
5 A 2 [4, 5, 6] 2
6 B 3 [7, 8, 9] 2
7 B 3 [7, 8, 9] 5
8 B 4 [1, 2, 3] 2
9 B 4 [1, 2, 3] 5
10 B 5 [3, 4, 5] 2
11 B 5 [3, 4, 5] 5
In [23]: df3[df3.apply(lambda x: x['ColC_y'] in x['ColC_x'], axis=1)]
Out[23]:
ColA ColB ColC_x ColC_y
0 A 1 [1, 2, 3] 3
2 A 1 [1, 2, 3] 2
4 A 2 [4, 5, 6] 6
8 B 4 [1, 2, 3] 2
11 B 5 [3, 4, 5] 5
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.