我有一个巨大的json文件，它的键调用类型（犯罪的类型），日期和时间（犯罪的日期）以及位置（地址或经纬度）以及其他带有值的键。我最感兴趣的是统计犯罪次数最多的日子，统计显示最多的呼叫类型以及显示最多的位置，该位置可以通过家庭住址或纬度和经度配对来衡量。 Python可能是最好的。 JSON上有350多种数据行的350多种呼叫类型。因此，每当您看到新的呼叫类型时，都应该为其创建一个新变量并对其进行跟踪
我尝试迭代将其扔进一个列表，但遇到了问题。当我的代码达到62 mb时，如何附加到我的代码上，我应该链接到文件吗？
这是数据示例
[{"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"},
我只想统计每种拨打电话的类型，拨打电话的时间，在哪个地点犯罪最多，在哪个日期犯罪最多等。
答案 0 :(得分：0)
使用pandas：
import pandas as pd
raw_df = pd.DataFrame(data)
df = raw_df.rename(columns=raw_df.iloc[0]).drop(0)
df
输出：
incident_num date_time day stno stdir1 StreetName ... call_type disposition beat priority lat long
1 P17060024503 6/14/2017 21:54 4 10 14TH ... 1151 O 521 2 32.7054489 -117.1518696
2 P17030051227 3/29/2017 22:24 4 10 14TH ... 1016 A 521 2 32.7054489 -117.1518696
3 P17060004814 6/3/2017 18:04 7 10 14TH ... 1016 A 521 2 32.7054489 -117.1518696
4 P17030029336 3/17/2017 10:57 6 10 14TH ... 1151 OT 521 2 32.7054489 -117.1518696
5 P17030005412 3/3/2017 23:45 6 10 15TH ... 911P CAN 521 2 32.7057215 -117.1503498
6 P17020016091 2/10/2017 8:23 6 10 15TH ... AU2 W 521 2 32.7057215 -117.1503498
7 P17040017368 4/11/2017 4:57 3 10 15TH ... 5150 CAN 521 2 32.7057215 -117.1503498
8 P17030048050 3/28/2017 6:30 3 10 15TH ... 1146 K 521 32.7057215 -117.1503498
9 P17060037341 6/22/2017 10:19 5 10 15TH ... 242 K 521 1 32.7057215 -117.1503498
10 P17060008467 6/5/2017 19:27 2 10 15TH ... 5150 K 521 2 32.7057215 -117.1503498
您可以运行的查询示例：
>>> df['call_type'].value_counts()
5150 2
1016 2
1151 2
242 1
911P 1
AU2 1
1146 1
答案 1 :(得分：0)
迭代json文件，并将所需字段存储在assosiatve数组中。您可以对其进行操作。
如果数据具有固定的列和结构，则可以将其存储在MySql之类的数据库中，并且可以通过简单的查询轻松地执行所需的操作。