which makes this task lot easierin [ ]from collections import counter in [ ]counts counter(time_zonesin [ ]counts most_common( out[ ][( 'america/new_york' )( '' )( 'america/chicago' )( 'america/los_angeles' )( 'america/denver' )( 'europe/london' )( 'asia/tokyo' )( 'pacific/honolulu' )( 'europe/madrid' )( 'america/sao_paulo' )counting time zones with pandas the main pandas data structure is the dataframewhich you can think of as representing table or spreadsheet of data creating dataframe from the original set of records is simplein [ ]from pandas import dataframeseries in [ ]import pandas as pd in [ ]frame dataframe(recordsin [ ]frame out[ ]int index entries to data columns_heartbeat_ non-null values non-null values al non-null values non-null values cy non-null values non-null values gr non-null values non-null values hc non-null values hh non-null values kw non-null values non-null values ll non-null values nk non-null values non-null values non-null values tz non-null values usa gov data from bit ly www it-ebooks info