python - Conditionally join a pandas DF on multiple indexes -


i have 2 pandas dataframes of different lengths, , need conditionally overwrite value 1 other when 2 columns match.

df1.val = df2.val df1.val == null , df1.key1 == df2.key1 , df1.key2 == df2.key2

df1 , df2 have different sets of columns , row lengths, save key1, key2, , val. thing that's guaranteed every (key1, key2) in df1, there 1 (key1, key2) in df2.

so far i've been going down path of

df1.loc[df1.val.isnull(), "val"] = df2.val

trying set indexes match, haven't got anywhere on that.

i know there's join-ons, haven't made progress on front either. syntactical appreciated.

edit data:

df1:

first  last   val random1 ... john   smith    4 x todd   smith    5 nan john   todd   nan z

df2:

first  last   val random2 ... john   smith    7 4 todd   smith    6 9 john   todd     3 3 eric   smith    5 2

result:

first  last   val random1 ...otherdf1cols... john   smith    4 x todd   smith    5 nan john   todd     3 z

set indices first, fillna

df1.set_index(['first', 'last']).fillna(df2.set_index(['first', 'last']))               val first last       john  smith  4.0 todd  smith  5.0 john  todd   3.0

use combine_first include stuff both dataframes

df1.set_index(['first', 'last']).combine_first(df2.set_index(['first', 'last']))               val first last       eric  smith  5.0 john  smith  4.0       todd   3.0 todd  smith  5.0

or, update val column, , limit rows first

d1 = df1.set_index(['first', 'last']) d2 = df2.set_index(['first', 'last']) print(d1.combine_first(d2[['val']]).loc[d1.index].reset_index())    first   last  val 0  john  smith  4.0 1  todd  smith  5.0 2  john   todd  3.0

an option using update

d1 = df1.set_index(['first', 'last']) d2 = df2.set_index(['first', 'last']) d1.update(d2.val, overwrite=false) d1.reset_index()    first   last  val 0  john  smith  4.0 1  todd  smith  5.0 2  john   todd  3.0

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