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hena fe awel lec fl course by2oul enna 3ndan 2 data set wahed infromation 3n kol ward mn el zip code w id bt3haa w kaman el address bt3 el center l kol ward w data set taniaa fehaa el population bta3 kol ward wl population f 2000 w 2010 bs w brdu column el zip code w column el center address fe column mtghyer w hwa el change ely 7assal el population zad wlaa b2aa 2oulyl hwa b2aa by2oul en el 2 tables are related with ward colun fa n2der n3mlhoum merge together matching ward for each row in ward tabel with ward number for each row in census tabel fa hwa ehnaa fl pandas 3ndhaa method helwaa esmaha merge btt3ml kda df.merge(df2, on= (el argument bt3 el on byshouf entaa 3awez t3ml ll two dataframe 3nd anhy column )) w b3dean by2ou; haga ana 3arfehaa w hya mdam 3mlna listed bl ward column first f hwa ely hyzhr fl awel b3d kda census hthzr b3dyha
the merge returns a DataFrame with 50 rows and 9 columns, where the returned rows have matching values for the ward column in both tables. This is called an inner join. y3ni delw2ty el inner join hy3ml return ll rows ely fehaa matching bs bl column ely 7atynha fl on b m3naa lw 3ndy data frame 30 row w dataframe tany 50 w ana 3aref en fe 23 row mn kol data frame shabh b3d w 3mlt inner join hy3mly return ll 23 row dool bs msh hyrg3 el b2y
by3ouly eny hl2y column feha suffix of _x or _y dah 3shan 2 tabels contains address column w zip column
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