如何在熊猫中进行“（df1＆not df2）”数据框合并？

Question

I have 2 pandas dataframes df1 & df2 with common columns/keys (x,y).

I want to merge do a "(df1 & not df2)" kind of merge on keys (x,y), meaning I want my code to return a dataframe containing rows with (x,y) only in df1 & not in df2.

SAS has an equivalent functionality

data final;
merge df1(in=a) df2(in=b);
by x y;
if a & not b;
run;

Who to replicate the same functionality in pandas elegantly? It would have been great if we can specify how="left-right" in merge().

Answer 1

I just upgraded to version 0.17.0 RC1 which was released 10 days ago. Just found out that pd.merge() have new argument in this new release called indicator=True to acheive this in pandonic way!!

df=pd.merge(df1,df2,on=['x','y'],how="outer",indicator=True)
df=df[df['_merge']=='left_only']

indicator: Add a column to the output DataFrame called _merge with information on the source of each row. _merge is Categorical-type and takes on a value of left_only for observations whose merge key only appears in 'left' DataFrame, right_only for observations whose merge key only appears in 'right' DataFrame, and both if the observation's merge key is found in both.

http://pandas-docs.github.io/pandas-docs-travis/merging.html#database-style-dataframe-joining-merging

Answer 2

Another way to do this is to use the index.

If i1 and i2 are indices (sets of indices) then i1.difference(i2) represent those indices in i1 and not in i2 . Then if df is a dataframe indexed by the same index type , for instance i1=df.index then pd.DataFrame(index=i1.difference(i2)).join(df) are those entries in df whose index is not in the index i2 .