python - Select rows in one DataFrame based on rows in another -

let's assume have large pandas dataframe dfbig columns param1, param2, ..., paramn, score, step, , smaller dataframe dfsmall columns param1, param2, ..., paramn (i.e. missing score , step columns).

i want select rows of dfbig values of columns param1, param2, ..., paramn match of row in dfsmall. there clean way of doing in pandas?

edit: give example, consider dataframe dfbig:

arch  | layers | score | time     | 1      | 0.3   | 10     | 1      | 0.6   | 20     | 1      | 0.7   | 30     | 2      | 0.4   | 10     | 2      | 0.5   | 20     | 2      | 0.6   | 30 b     | 1      | 0.1   | 10 b     | 1      | 0.2   | 20 b     | 1      | 0.7   | 30 b     | 2      | 0.7   | 10 b     | 2      | 0.8   | 20 b     | 2      | 0.8   | 30 

let's imagine model specified pair (arch, layers). want query dfbig , time series scores on time best performing models arch , arch b.

following edchum's answer below, take best solution procedurally:

modelcolumns = [col col in dfbigcol if col not in ["time", "score"]]     groupedbest = dfbig.groupby("arch").score.max() dfsmall = pd.dataframe(groupedbest).reset_index()[modelcolumns] dfbest = pd.merge(dfsmall, dfbig) 

which yields:

arch  | layers | score | time     | 1      | 0.3   | 10     | 1      | 0.6   | 20     | 1      | 0.7   | 30 b     | 2      | 0.7   | 10 b     | 2      | 0.8   | 20 b     | 2      | 0.8   | 30 

if there's better way this, i'm happy hear it.

if understand question correctly should able call merge on dfbig , pass dfsmall matches in aligned columns , return rows.

example:

in [71]:  dfbig = pd.dataframe({'a':np.arange(100), 'b':np.arange(100), 'c':np.arange(100)}) dfsmall = pd.dataframe({'a':[3,4,5,6]}) dfbig.merge(dfsmall) out[71]:     b  c 0  3  3  3 1  4  4  4 2  5  5  5 3  6  6  6