8/28/2023 0 Comments Pandas merge dataframes![]() ![]() > timeit.timeit(setup=setup, stmt="df = df_1.merge(df_2, on='date'). > timeit.timeit(setup=setup, stmt="df_merged = reduce(lambda left,right: pd.merge(left,right,on=, how='outer'), dfs).fillna('void')", number=1000) > timeit.timeit(setup=setup, stmt="reduce(lambda left,right: pd.merge(left,right,on=, how='outer'), dfs)", number=1000) You could also use rge like this df = df1.merge(df2).merge(df3)Ĭomparing performance of this method to the currently accepted answer import timeitĭf_1 = pd.DataFrame() Time-series friendly merging provided in pandas Along the way, you will also learn a few tricks which you require before and after joining. pd.DataFrame.to_csv(df_merged, 'merged.txt', sep=',', na_rep='.', index=False) Merge DataFrames on specific keys by different join logics like left-join, inner-join, etc. Then write the merged data to the csv file if desired.
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