Question

i have a huge json file , it has a key call type(the type of crime commited), date and time(date crime was commited) , and location(address or lat&long) among other keys with values. Im mostly interested in counting the days with the most crimes , counting what call types show up the most, and what location shows up the most also, the location can measure by the home address or pairing the latitude and longitude together. Python would probably be best . THERES OVER 350 TYPES OF CALL TYPES ON A JSON WITH OVER 350K DATA ROWS. So everything time you see a new call type it should like create a new variable for that and keep track of it

i tried iterating threw it like a list but having issues . how can i attach to my code when its 62 mb , should i link to a file?

this is an example of data

[{"A": "incident_num", "B": "date_time", "C": "day", "D": "stno", "E": "stdir1", "F": "StreetName", "G": "streettype", "H": "FullAddress", "I": "call_type", "J": "disposition", "K": "beat", "L": "priority", "M": "lat", "N": "long"},
{"A": "P17060024503", "B": "6/14/2017 21:54", "C": "4", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1151", "J": "O", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},
{"A": "P17030051227", "B": "3/29/2017 22:24", "C": "4", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1016", "J": "A", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},
{"A": "P17060004814", "B": "6/3/2017 18:04", "C": "7", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1016", "J": "A", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},
{"A": "P17030029336", "B": "3/17/2017 10:57", "C": "6", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1151", "J": "OT", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},
{"A": "P17030005412", "B": "3/3/2017 23:45", "C": "6", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "911P", "J": "CAN", "K": "521", "L": "2", "M": "32.7057215", "N": "-117.1503498"},
{"A": "P17020016091", "B": "2/10/2017 8:23", "C": "6", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "AU2", "J": "W", "K": "521", "L": "2", "M": "32.7057215", "N": "-117.1503498"},
{"A": "P17040017368", "B": "4/11/2017 4:57", "C": "3", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "5150", "J": "CAN", "K": "521", "L": "2", "M": "32.7057215", "N": "-117.1503498"},
{"A": "P17030048050", "B": "3/28/2017 6:30", "C": "3", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "1146", "J": "K", "K": "521", "L": "", "M": "32.7057215", "N": "-117.1503498"},
{"A": "P17060037341", "B": "6/22/2017 10:19", "C": "5", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "242", "J": "K", "K": "521", "L": "1", "M": "32.7057215", "N": "-117.1503498"},
{"A": "P17060008467", "B": "6/5/2017 19:27", "C": "2", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "5150", "J": "K", "K": "521", "L": "2", "M": "32.7057215", "N": "-117.1503498"},

i just want stats for like each call type that was made and how much time it was made for each Key I , how calls where made for each address Key F , and the calls for each date time that shows up key B? YOU CAN IGNORE OTHER COLUMNS , python would be best but if other language is fine

Answer 1

import pandas as pd

data= [{"A": "incident_num", "B": "date_time", "C": "day", "D": "stno", "E": "stdir1", "F": "StreetName", "G": "streettype", "H": "FullAddress", "I": "call_type", "J": "disposition", "K": "beat", "L": "priority", "M": "lat", "N": "long"},
{"A": "P17060024503", "B": "6/14/2017 21:54", "C": "4", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1151", "J": "O", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},
{"A": "P17030051227", "B": "3/29/2017 22:24", "C": "4", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1016", "J": "A", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},
{"A": "P17060004814", "B": "6/3/2017 18:04", "C": "7", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1016", "J": "A", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},
{"A": "P17030029336", "B": "3/17/2017 10:57", "C": "6", "D": "10", "E": "", "F": "14TH", "G": "ST", "H": "10 14TH ST, San Diego, CA", "I": "1151", "J": "OT", "K": "521", "L": "2", "M": "32.7054489", "N": "-117.1518696"},
{"A": "P17030005412", "B": "3/3/2017 23:45", "C": "6", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "911P", "J": "CAN", "K": "521", "L": "2", "M": "32.7057215", "N": "-117.1503498"},
{"A": "P17020016091", "B": "2/10/2017 8:23", "C": "6", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "AU2", "J": "W", "K": "521", "L": "2", "M": "32.7057215", "N": "-117.1503498"},
{"A": "P17040017368", "B": "4/11/2017 4:57", "C": "3", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "5150", "J": "CAN", "K": "521", "L": "2", "M": "32.7057215", "N": "-117.1503498"},
{"A": "P17030048050", "B": "3/28/2017 6:30", "C": "3", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "1146", "J": "K", "K": "521", "L": "", "M": "32.7057215", "N": "-117.1503498"},
{"A": "P17060037341", "B": "6/22/2017 10:19", "C": "5", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "242", "J": "K", "K": "521", "L": "1", "M": "32.7057215", "N": "-117.1503498"},
{"A": "P17060008467", "B": "6/5/2017 19:27", "C": "2", "D": "10", "E": "", "F": "15TH", "G": "ST", "H": "10 15TH ST, San Diego, CA", "I": "5150", "J": "K", "K": "521", "L": "2", "M": "32.7057215", "N": "-117.1503498"}]

df=pd.Dataframe(data)

lst=list(df['call_type'])

print(lst)