[英]Find common elements in two dataframes
I have two dataframes df1
and df2
having the same columns.我有两个数据df1
和df2
具有相同的列。 I would like to find the elements within a column that are in common.我想在列中找到共同的元素。 For example例如
df1 : df1 :
Col1 Col2 Nam1 Nam2 Net
AD AS AS ADS AB
BF SA WQ AFW AF
RW KJ IQ QIE LK
df2 : df2 :
Col1 Col2 Nam1 Nam2 Net
RW WQ HF HGJ AB
BF AS DD VCC LJ
RW KJ IQ ADS JH
DS QW LJ NB LK
I would like to have the following (on Net)我想要以下(在网上)
Col1 Col2 Nam1 Nam2 Net
AD AS AS ADS AB
RW WQ HF HGJ AB
RW KJ IQ QIE LK
DS QW LJ NB LK
I have tried as follows:我试过如下:
df=pd.merge(df1, df2, on='Net', how='inner')
but it duplicates all the columns' name (_x and _y) and also it seems not extracting only the elements in common.但它重复了所有列的名称(_x 和 _y),而且它似乎没有只提取共同的元素。
IIUC, you just want those rows which share the same net value? IIUC,您只想要那些共享相同净值的行吗? You can start with,你可以从,
vals = set(df1['Net']).intersection(df2['Net'])
print (vals)
# {'AB', 'LK'}
Now, filter out those values and concatenate:现在,过滤掉这些值并连接:
pd.concat([
df1.query('Net in @vals'),
df2.query('Net in @vals')], ignore_index=True)
Col1 Col2 Nam1 Nam2 Net
0 AD AS AS ADS AB
1 RW KJ IQ QIE LK
2 RW WQ HF HGJ AB
3 DS QW LJ NB LK
One explicit way to do it would be:一种明确的方法是:
common_items = set(df1['Net']) & set(df2['Net'])
df1_common = df1[df1['Net'].isin(common_items)]
Col1 Col2 Nam1 Nam2 Net
AD AS AS ADS AB
RW KJ IQ QIE LK
df2_common = df2[df2['Net'].isin(common_items)]
Col1 Col2 Nam1 Nam2 Net
RW WQ HF HGJ AB
DS QW LJ NB LK
pd.concat([df1_common, df2_common])
Col1 Col2 Nam1 Nam2 Net
AD AS AS ADS AB
RW WQ HF HGJ AB
RW KJ IQ QIE LK
DS QW LJ NB LK
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