I have two different dataframes, one is date combinations, and one is city pairs:
df_date_combinations:
------------------- -------------------
| fs_date| ss_date|
------------------- -------------------
|2022-06-01T00:00:00|2022-06-02T00:00:00|
|2022-06-01T00:00:00|2022-06-03T00:00:00|
|2022-06-01T00:00:00|2022-06-04T00:00:00|
------------------- -------------------
city pairs:
--------- -------------- --------- --------------
|fs_origin|fs_destination|ss_origin|ss_destination|
--------- -------------- --------- --------------
| TLV| NYC| NYC| TLV|
| TLV| ROM| ROM| TLV|
| TLV| BER| BER| TLV|
--------- -------------- --------- --------------
I want to combine them so I will have the following dataframe:
---------- ---------- --------- -------------- --------- --------------
| fs_date| ss_date|fs_origin|fs_destination|ss_origin|ss_destination|
---------- ---------- --------- -------------- --------- --------------
|2022-06-01|2022-06-02| TLV| NYC| NYC| TLV|
|2022-06-01|2022-06-03| TLV| NYC| NYC| TLV|
|2022-06-01|2022-06-04| TLV| NYC| NYC| TLV|
|2022-06-01|2022-06-02| TLV| ROM| ROM| TLV|
|2022-06-01|2022-06-03| TLV| ROM| ROM| TLV|
|2022-06-01|2022-06-04| TLV| ROM| ROM| TLV|
|2022-06-01|2022-06-02| TLV| BER| BER| TLV|
|2022-06-01|2022-06-03| TLV| BER| BER| TLV|
|2022-06-01|2022-06-04| TLV| BER| BER| TLV|
---------- ---------- --------- -------------- --------- --------------
Thanks!
CodePudding user response：
sounds like a cross join.
df1.crossJoin(df2)
CodePudding user response：
Pandas actually has built-in methods to do this, we use concat to concatenate the dataframes. You can read how to do this here:
The part that is pertinent to you would be:
pd.concat([df_date_combinations, city_pairs], axis = 1)
Hope this helps!