I am new to this website and came here because I am really struggling with a problem of extracting information from a JSON file. The tricky part is that there are variable number of fields, so I can't get away with simple syntax.
我是这个网站的新手来到这里，因为我真的很难解决从JSON文件中提取信息的问题。棘手的部分是存在可变数量的字段，因此我无法使用简单的语法。
Here's a sample code:
这是一个示例代码：
{
"addresses": {
"@count": "1",
"address_name": {
"address_spec": {
"@addr_no": "1",
"full_address": "Tel Aviv Univ, Eitan Berglas Sch Econ, IL-69978 Tel Aviv, Israel",
"organizations": {
"@count": "2",
"organization": [
"Tel Aviv Univ",
{
"@pref": "Y",
"#text": "Tel Aviv University"
}
]
},
"suborganizations": {
"@count": "1",
"suborganization": "Eitan Berglas Sch Econ"
},
"city": "Tel Aviv",
"country": "Israel",
"zip": {
"@location": "BC",
"#text": "IL-69978"
}
}
}
},
"category_info": {
"headings": {
"@count": "1",
"heading": "Social Sciences"
},
"subjects": {
"@count": "3",
"subject": [
{
"@ascatype": "traditional",
"#text": "Economics"
},
{
"@ascatype": "extended",
"#text": "Business & Economics"
},
{
"@ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"@count": "1",
"address_name": {
"address_spec": {
"@addr_no": "1",
"full_address": "MIT, Cambridge, MA 02139 USA",
"organizations": {
"@count": "2",
"organization": [
"MIT",
{
"@pref": "Y",
"#text": "Massachusetts Institute of Technology (MIT)"
}
]
},
"city": "Cambridge",
"state": "MA",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "02139"
}
}
}
},
"category_info": {
"headings": {
"@count": "1",
"heading": "Social Sciences"
},
"subjects": {
"@count": "3",
"subject": [
{
"@ascatype": "traditional",
"#text": "Economics"
},
{
"@ascatype": "extended",
"#text": "Business & Economics"
},
{
"@ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"@count": "2",
"address_name": [
{
"address_spec": {
"@addr_no": "1",
"full_address": "Univ Kentucky, Lexington, KY 40506 USA",
"organizations": {
"@count": "2",
"organization": [
"Univ Kentucky",
{
"@pref": "Y",
"#text": "University of Kentucky"
}
]
},
"city": "Lexington",
"state": "KY",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "40506"
}
}
},
{
"address_spec": {
"@addr_no": "2",
"full_address": "Univ Bonn, ZEI, D-5300 Bonn, Germany",
"organizations": {
"@count": "2",
"organization": [
"Univ Bonn",
{
"@pref": "Y",
"#text": "University of Bonn"
}
]
},
"suborganizations": {
"@count": "1",
"suborganization": "ZEI"
},
"city": "Bonn",
"country": "Germany",
"zip": {
"@location": "BC",
"#text": "D-5300"
}
}
}
]
},
"category_info": {
"headings": {
"@count": "1",
"heading": "Social Sciences"
},
"subjects": {
"@count": "3",
"subject": [
{
"@ascatype": "traditional",
"#text": "Economics"
},
{
"@ascatype": "extended",
"#text": "Business & Economics"
},
{
"@ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"@count": "1",
"address_name": {
"address_spec": {
"@addr_no": "1",
"full_address": "Harvard Univ, Cambridge, MA 02138 USA",
"organizations": {
"@count": "2",
"organization": [
"Harvard Univ",
{
"@pref": "Y",
"#text": "Harvard University"
}
]
},
"city": "Cambridge",
"state": "MA",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "02138"
}
}
}
},
"category_info": {
"headings": {
"@count": "1",
"heading": "Social Sciences"
},
"subjects": {
"@count": "3",
"subject": [
{
"@ascatype": "traditional",
"#text": "Economics"
},
{
"@ascatype": "extended",
"#text": "Business & Economics"
},
{
"@ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"@count": "3",
"address_name": [
{
"address_spec": {
"@addr_no": "1",
"full_address": "Columbia Univ, New York, NY 10027 USA",
"organizations": {
"@count": "2",
"organization": [
"Columbia Univ",
{
"@pref": "Y",
"#text": "Columbia University"
}
]
},
"city": "New York",
"state": "NY",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "10027"
}
}
},
{
"address_spec": {
"@addr_no": "2",
"full_address": "NYU, New York, NY USA",
"organizations": {
"@count": "2",
"organization": [
"NYU",
{
"@pref": "Y",
"#text": "New York University"
}
]
},
"city": "New York",
"state": "NY",
"country": "USA"
}
},
{
"address_spec": {
"@addr_no": "3",
"full_address": "Univ Pompeu Fabra, Barcelona, Spain",
"organizations": {
"@count": "2",
"organization": [
"Univ Pompeu Fabra",
{
"@pref": "Y",
"#text": "Pompeu Fabra University"
}
]
},
"city": "Barcelona",
"country": "Spain"
}
}
]
},
"category_info": {
"headings": {
"@count": "1",
"heading": "Social Sciences"
},
"subjects": {
"@count": "3",
"subject": [
{
"@ascatype": "traditional",
"#text": "Economics"
},
{
"@ascatype": "extended",
"#text": "Business & Economics"
},
{
"@ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"@count": "2",
"address_name": [
{
"address_spec": {
"@addr_no": "1",
"full_address": "Univ Chicago, Chicago, IL 60637 USA",
"organizations": {
"@count": "2",
"organization": [
"Univ Chicago",
{
"@pref": "Y",
"#text": "University of Chicago"
}
]
},
"city": "Chicago",
"state": "IL",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "60637"
}
}
},
{
"address_spec": {
"@addr_no": "2",
"full_address": "Amer Bar Fdn, Chicago, IL 60611 USA",
"organizations": {
"@count": "1",
"organization": "Amer Bar Fdn"
},
"city": "Chicago",
"state": "IL",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "60611"
}
}
}
]
},
"category_info": {
"headings": {
"@count": "1",
"heading": "Social Sciences"
},
"subjects": {
"@count": "3",
"subject": [
{
"@ascatype": "traditional",
"#text": "Economics"
},
{
"@ascatype": "extended",
"#text": "Business & Economics"
},
{
"@ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"@count": "2",
"address_name": [
{
"address_spec": {
"@addr_no": "1",
"full_address": "Ohio State Univ, Columbus, OH 43210 USA",
"organizations": {
"@count": "2",
"organization": [
"Ohio State Univ",
{
"@pref": "Y",
"#text": "Ohio State University"
}
]
},
"city": "Columbus",
"state": "OH",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "43210"
}
}
},
{
"address_spec": {
"@addr_no": "2",
"full_address": "Harvard Univ, Cambridge, MA 02138 USA",
"organizations": {
"@count": "2",
"organization": [
"Harvard Univ",
{
"@pref": "Y",
"#text": "Harvard University"
}
]
},
"city": "Cambridge",
"state": "MA",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "02138"
}
}
}
]
},
"category_info": {
"headings": {
"@count": "1",
"heading": "Social Sciences"
},
"subjects": {
"@count": "3",
"subject": [
{
"@ascatype": "traditional",
"#text": "Economics"
},
{
"@ascatype": "extended",
"#text": "Business & Economics"
},
{
"@ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"@count": "1",
"address_name": {
"address_spec": {
"@addr_no": "1",
"full_address": "Univ Chicago, Chicago, IL 60637 USA",
"organizations": {
"@count": "2",
"organization": [
"Univ Chicago",
{
"@pref": "Y",
"#text": "University of Chicago"
}
]
},
"city": "Chicago",
"state": "IL",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "60637"
}
}
}
},
"category_info": {
"headings": {
"@count": "1",
"heading": "Social Sciences"
},
"subjects": {
"@count": "3",
"subject": [
{
"@ascatype": "traditional",
"#text": "Economics"
},
{
"@ascatype": "extended",
"#text": "Business & Economics"
},
{
"@ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"@count": "2",
"address_name": [
{
"address_spec": {
"@addr_no": "1",
"full_address": "Wissensch Zentrum Berlin Sozialforsch, D-1000 Berlin, Germany",
"organizations": {
"@count": "1",
"organization": "Wissensch Zentrum Berlin Sozialforsch"
},
"city": "Berlin",
"country": "Germany",
"zip": {
"@location": "BC",
"#text": "D-1000"
}
}
},
{
"address_spec": {
"@addr_no": "2",
"full_address": "Harvard Univ, Dept Govt, Cambridge, MA 02138 USA",
"organizations": {
"@count": "2",
"organization": [
"Harvard Univ",
{
"@pref": "Y",
"#text": "Harvard University"
}
]
},
"suborganizations": {
"@count": "1",
"suborganization": "Dept Govt"
},
"city": "Cambridge",
"state": "MA",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "02138"
}
}
}
]
},
"category_info": {
"headings": {
"@count": "1",
"heading": "Social Sciences"
},
"subjects": {
"@count": "3",
"subject": [
{
"@ascatype": "traditional",
"#text": "Economics"
},
{
"@ascatype": "extended",
"#text": "Business & Economics"
},
{
"@ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"@count": "2",
"address_name": [
{
"address_spec": {
"@addr_no": "1",
"full_address": "NYU, CV Starr Ctr Appl Econ, New York, NY 10003 USA",
"organizations": {
"@count": "2",
"organization": [
"NYU",
{
"@pref": "Y",
"#text": "New York University"
}
]
},
"suborganizations": {
"@count": "1",
"suborganization": "CV Starr Ctr Appl Econ"
},
"city": "New York",
"state": "NY",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "10003"
}
}
},
{
"address_spec": {
"@addr_no": "2",
"full_address": "Princeton Univ, Princeton, NJ 08544 USA",
"organizations": {
"@count": "2",
"organization": [
"Princeton Univ",
{
"@pref": "Y",
"#text": "Princeton University"
}
]
},
"city": "Princeton",
"state": "NJ",
"country": "USA",
"zip": {
"@location": "AP",
"#text": "08544"
}
}
}
]
},
"category_info": {
"headings": {
"@count": "1",
"heading": "Social Sciences"
},
"subjects": {
"@count": "3",
"subject": [
{
"@ascatype": "traditional",
"#text": "Economics"
},
{
"@ascatype": "extended",
"#text": "Business & Economics"
},
{
"@ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
What I was hoping to extract is a country for each of the records (some records have more than one country, which seems to be causing the problem). So my naive approach was to say:
我希望提取的是每个记录的国家（一些记录有多个国家，这似乎导致了这个问题）。所以我天真的做法是说：
.static_data."fullrecord_metadata".addresses.address_name.country
This however gives me several errors (null has no keys, and cannot index array with string). Checking using the keys command:
然而，这给了我几个错误（null没有键，也不能用字符串索引数组）。使用keys命令检查：
.static_data."fullrecord_metadata".addresses.address_name | keys
I can see that it's seems there's a problem with the way the data is structured...
我可以看到，数据结构的方式似乎存在问题......
So, could you suggest if I can actually extract the list of countries for each entry using jq? Thank you!
那么，您能否建议我是否可以使用jq实际提取每个条目的国家/地区列表？谢谢！
3
For each input top-level JSON entity, the following filter will recursively examine all the objects to see if they have a "country" key, and it will then report the distinct "country" values for that top-level entity:
对于每个输入顶级JSON实体，以下过滤器将递归检查所有对象以查看它们是否具有“country”键，然后它将报告该顶级实体的不同“country”值：
jq -c '[.. | if type == "object" and has("country")
then .country
else empty end] | unique'
["Israel"]
["USA"]
["Germany","USA"]
["USA"]
["Spain","USA"]
["USA"]
["USA"]
["USA"]
["Germany","USA"]
["USA"]
Here's a filter that will produce the same results in your example, though it is not exactly equivalent:
这是一个过滤器，可以在您的示例中生成相同的结果，但它不完全等效：
[.. | .country? // empty] | unique
[Exercise for the interested reader: what is the difference? :-) ]
[为感兴趣的读者练习：有什么区别？ :-)]
1
Here is a solution which uses a function to handle the variation in .address_name
这是一个使用函数来处理.address_name中的变体的解决方案
def address_specs:
if type == "array" then .[].address_spec else .address_spec end
;
.addresses | .address_name | [address_specs | .country] | unique
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