[英]Compare columns in Pandas between two unequal size Dataframes for condition check
I have two pandas DF.我有两只熊猫 DF。 Of unequal sizes.
大小不等。 For example :
例如 :
Df1
id value
a 2
b 3
c 22
d 5
Df2
id value
c 22
a 2
No I want to extract from DF1 those rows which has the same id as in DF2.不,我想从 DF1 中提取与 DF2 具有相同 id 的那些行。 Now my first approach is to run 2 for loops, with something like :
现在我的第一种方法是运行 2 个 for 循环,类似于:
x=[]
for i in range(len(DF2)):
for j in range(len(DF1)):
if DF2['id'][i] == DF1['id'][j]:
x.append(DF1.iloc[j])
Now this is okay, but for 2 files of 400,000 lines in one and 5,000 in another, I need an efficient Pythonic+Pnadas way现在这没问题,但是对于 2 个 400,000 行的文件和 5,000 行的另一个文件,我需要一种高效的 Pythonic+Pnadas 方式
You can concat the dataframes , then check if all the elements are duplicated
or not , then drop_duplicates
and keep just the first occurrence:您可以连接数据帧,然后检查所有元素是否
duplicated
,然后drop_duplicates
并仅保留第一次出现:
m = pd.concat((df1,df2))
m[m.duplicated('id',keep=False)].drop_duplicates()
id value
0 a 2
2 c 22
你可以试试这个:
df = df1[df1.set_index(['id']).index.isin(df2.set_index(['id']).index)]
import pandas as pd
data1={'id':['a','b','c','d'],
'value':[2,3,22,5]}
data2={'id':['c','a'],
'value':[22,2]}
df1=pd.DataFrame(data1)
df2=pd.DataFrame(data2)
finaldf=pd.concat([df1,df2],ignore_index=True)
Output after concat连接后输出
id value
0 a 2
1 b 3
2 c 22
3 d 5
4 c 22
5 a 2
Final Ouput最终输出
finaldf.drop_duplicates()
id value
0 a 2
1 b 3
2 c 22
3 d 5
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