Selecting rows in one dataframe based on data in another dataframe in Python Pandas -

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i have 2 dataframes created pandas. first 1 has co-occurrences of items happening in years:

   date item1 item2 0  1975         b 1  1976     b     c 2  1977     b     3  1977         b 4  1978     c     d 5  1979     e     f 6  1980         f

the second 1 has birthdates of items:

birthdate item     1975     1975     b 1976     c 1978     d 1979     f 1979     e

now, want set age variable, example:

age = 2

and populate third dataframe (alternative transform first one) version of first 1 keeping rows of co-occurrences happened when item1 below defined 'age'.

you merge dataframes - similar join in sql

import pandas  data = [     [1975,'a','b'],     [1976,'b','c'],     [1977,'b','a'],     [1977,'a','b'],     [1978,'c','d'],     [1979,'e','f'],     [1980,'a','f'], ]  birthdate = [     [1975,'a'],     [1975,'b'],     [1976,'c'],     [1978,'d'],     [1979,'f'],     [1979,'e'] ]  df1 = pandas.dataframe(data, columns = ['date', 'item1', 'item2']) df2 = pandas.dataframe(birthdate, columns = ['birthdate', 'item'])  #print df1 #print df2  newdf = pandas.merge(left=df1, right=df2, left_on='item1', right_on='item')  print newdf  print newdf[ newdf['birthdate'] > 1975 ]

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   date item1 item2  birthdate item 0  1975         b       1975    1  1977         b       1975    2  1980         f       1975    3  1976     b     c       1975    b 4  1977     b           1975    b 5  1978     c     d       1976    c 6  1979     e     f       1979    e     date item1 item2  birthdate item 5  1978     c     d       1976    c 6  1979     e     f       1979    e

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