now easy to access individual values within records by passing string for the key you wish to accessin [ ]records[ ]['tz'out[ ] 'america/new_yorkthe here in front of the quotation stands for unicodea standard form of string encoding note that ipython shows the time zone string object representation here rather than its print equivalentin [ ]print records[ ]['tz'america/new_york counting time zones in pure python suppose we were interested in the most often-occurring time zones in the data set (the tz fieldthere are many ways we could do this firstlet' extract list of time zones again using list comprehensionin [ ]time_zones [rec['tz'for rec in recordskeyerror traceback (most recent call last/home/wesm/book_scripts/whettingin (---- time_zones [rec['tz'for rec in recordskeyerror'tzoopsturns out that not all of the records have time zone field this is easy to handle as we can add the check if 'tzin rec at the end of the list comprehensionin [ ]time_zones [rec['tz'for rec in records if 'tzin recin [ ]time_zones[: out[ ][ 'america/new_york' 'america/denver' 'america/new_york' 'america/sao_paulo' 'america/new_york' 'america/new_york' 'europe/warsaw' '' '' ''just looking at the first time zones we see that some of them are unknown (emptyyou can filter these out also but 'll leave them in for now nowto produce counts by time zone 'll show two approachesthe harder way (using just the python standard libraryand the easier way (using pandasone way to do the counting is to use dict to store counts while we iterate through the time zonesdef get_counts(sequence)counts { usa gov data from bit ly www it-ebooks info