How to do intersection match between 2 DataFrames in Pandas? -


assume exists 2 dataframes a , b following

a:

a b b c c

b:

1 2 3 4

how produce c dataframe like

a   1 2   3 4 b  b  1 2 b  b  3 4 c  c  1 2 c  c  3 4

is there function in pandas can operation?

first values has unique in each dataframe.

i think need product:

from  itertools import product  = pd.dataframe({'a':list('abc')}) b = pd.dataframe({'a':[1,2]})  c = pd.dataframe(list(product(a['a'], b['a']))) print (c)    0  1 0   1 1   2 2  b  1 3  b  2 4  c  1 5  c  2

pandas pure solutions multiindex.from_product:

mux = pd.multiindex.from_product([a['a'], b['a']])  c = pd.dataframe(mux.values.tolist()) print (c)    0  1 0   1 1   2 2  b  1 3  b  2 4  c  1 5  c  2 
c = mux.to_frame().reset_index(drop=true) print (c)    0  1 0   1 1   2 2  b  1 3  b  2 4  c  1 5  c  2

solution cross join merge , column filled same scalars assign:

df = pd.merge(a.assign(tmp=1), b.assign(tmp=1), on='tmp').drop('tmp', 1) df.columns = ['a','b'] print (df)     b 0   1 1   2 2  b  1 3  b  2 4  c  1 5  c  2

edit:

a = pd.dataframe({'a':list('abc'), 'b':list('abc')}) b = pd.dataframe({'a':[1,3], 'c':[2,4]})  print (a)     b 0   1  b  b 2  c  c  print (b)     c 0  1  2 1  3  4  c = pd.merge(a.assign(tmp=1), b.assign(tmp=1), on='tmp').drop('tmp', 1) c.columns = list('abcd') print (c)     b  c  d 0    1  2 1    3  4 2  b  b  1  2 3  b  b  3  4 4  c  c  1  2 5  c  c  3  4

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