Python pandas: compare data and put together -

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im using pandas read out csv-file , xlsx-file. both files have 1 common column numbers. save both datasets in 2 seperate lists. want check columns common values , add dataset second list first. criteria values must match. hope understand want do.

here lists: list 1:

       fak-art    fak-dat  leist-dat      kd-crm mw-bw       eq-nr material  \ 1         zpaf 2015-05-18 2015-05-31         tmd     e  1003594714  g230ets    2         zpaf 2015-05-18 2015-05-31         tmd     b  1000943473  g230ets    3         zpaf 2015-05-18 2015-05-31         tmd     e  1000943608  g230ets    4         zpaf 2015-05-18 2015-05-31         tmd     b  1000943704  g230ets    5         zpaf 2015-05-18 2015-05-31         tmd     e  1000943823  g230ets    6         zpaf 2015-05-18 2015-05-31         tmd     b  1000943985  g230ets    7         zpaf 2015-05-18 2015-05-31         tmd     e  1000954774  g230ets    8         zpaf 2015-05-18 2015-05-31         tmd     b  1000954790  g230ets    9         zpaf 2015-05-18 2015-05-31         tmd     e  1000955082  g230ets    10        zpaf 2015-05-18 2015-05-31         tmd     b  1000955097  g230ets    11        zpaf 2015-05-18 2015-05-31         tmd     e  1001415563  g230ets    12        zpaf 2015-05-18 2015-05-31         tmd     b  1001415566  g230ets    13        zpaf 2015-05-18 2015-05-31         tmd     e  1001415569  g230ets    14        zpaf 2015-05-18 2015-05-31         tmd     b  1003116180  g230ets    15        zpaf 2015-05-18 2015-05-31         tmd     e  1003189748  g230ets    16        zpaf 2015-05-18 2015-05-31         tmd     b  1003189752  g230ets    17        zpaf 2015-05-18 2015-05-31         tmd     e  1003189753  g230ets

list 2

             eq-nr         ta 0       1003594714  sonstiges 1       1000943473        nan 2       1000943608  sonstiges 3       1000943704  sonstiges 4       1000943823  sonstiges 5       1000943985  sonstiges 6       1000954774       fmed 7       1000954790       fmed 8       1000955082        sdh 9       1000955097        nan 10      1001415563  sonstiges 11      1001415566  sonstiges 12      1001415569  sonstiges 13      1001496157        nan 14      1003116180        nan 15      1003189748        nan 16      1003189752        nan 17      1003189753        nan

now need hint how solve this. googled lot , didnt find solution problem. great if help.

use merge:

print (pd.merge(df1, df2, on='eq-nr', how='left'))     fak-art     fak-dat   leist-dat kd-crm mw-bw       eq-nr material  \ 0     zpaf  2015-05-18  2015-05-31    tmd     e  1003594714  g230ets    1     zpaf  2015-05-18  2015-05-31    tmd     b  1000943473  g230ets    2     zpaf  2015-05-18  2015-05-31    tmd     e  1000943608  g230ets    3     zpaf  2015-05-18  2015-05-31    tmd     b  1000943704  g230ets    4     zpaf  2015-05-18  2015-05-31    tmd     e  1000943823  g230ets    5     zpaf  2015-05-18  2015-05-31    tmd     b  1000943985  g230ets    6     zpaf  2015-05-18  2015-05-31    tmd     e  1000954774  g230ets    7     zpaf  2015-05-18  2015-05-31    tmd     b  1000954790  g230ets    8     zpaf  2015-05-18  2015-05-31    tmd     e  1000955082  g230ets    9     zpaf  2015-05-18  2015-05-31    tmd     b  1000955097  g230ets    10    zpaf  2015-05-18  2015-05-31    tmd     e  1001415563  g230ets    11    zpaf  2015-05-18  2015-05-31    tmd     b  1001415566  g230ets    12    zpaf  2015-05-18  2015-05-31    tmd     e  1001415569  g230ets    13    zpaf  2015-05-18  2015-05-31    tmd     b  1003116180  g230ets    14    zpaf  2015-05-18  2015-05-31    tmd     e  1003189748  g230ets    15    zpaf  2015-05-18  2015-05-31    tmd     b  1003189752  g230ets    16    zpaf  2015-05-18  2015-05-31    tmd     e  1003189753  g230ets                ta   0   sonstiges   1         nan   2   sonstiges   3   sonstiges   4   sonstiges   5   sonstiges   6        fmed   7        fmed   8         sdh   9         nan   10  sonstiges   11  sonstiges   12  sonstiges   13        nan   14        nan   15        nan   16        nan

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