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How to group a json by a nested key using Python?

Lets say we have a json object in Python:
myJson = [
{
"id": "123",
"name": "alex",
"meta": {
"city": "boston"
}
},
{
"id": "234",
"name": "mike",
"meta": {
"city": "seattle"
}
},
{
"id": "345",
"name": "jess",
"meta": {
"city": "boston"
}
}
]
What is the most efficient way to group this data by city, so that we end up with a json in which we group the data by city such that we end up with a json as:
myNewJson = [
{
"city": "boston",
"people": [ ... ... ]
},
{
"city": "seattle",
"people": [ ... ]
}
]
... in which the content of the people are included in "people" key.
Thanks!

Try:
myJson = [
{"id": "123", "name": "alex", "meta": {"city": "boston"}},
{"id": "234", "name": "mike", "meta": {"city": "seattle"}},
{"id": "345", "name": "jess", "meta": {"city": "boston"}},
]
out = {}
for d in myJson:
out.setdefault(d["meta"]["city"], []).append(d["name"])
out = [{"city": k, "people": v} for k, v in out.items()]
print(out)
Prints:
[
{"city": "boston", "people": ["alex", "jess"]},
{"city": "seattle", "people": ["mike"]},
]

Seems like a dictionary could work. Use city names as the keys, and a list as the value. Then at the end, go through the dictionary and convert it to a list.
myJson = [
{
"id": "123",
"name": "alex",
"meta": {
"city": "boston"
}
},
{
"id": "234",
"name": "mike",
"meta": {
"city": "seattle"
}
},
{
"id": "345",
"name": "jess",
"meta": {
"city": "boston"
}
}
]
d = dict() # dictionary of {city: list of people}
for e in myJson:
city = e['meta']['city']
if city not in d:
d[city] = list()
d[city].append(e['name'])
# convert dictionary to list of json
result = list()
for key, val in d.items():
result.append({'city': key, 'people': val})
print(result)

Using pandas to convert csv into nested json with dynamic strucutre

I am new to python and now want to convert a csv file into json file. Basically the json file is nested with dynamic structure, the structure will be defined using the csv header.
From csv input:
ID, Name, person_id/id_type, person_id/id_value,person_id_expiry_date,additional_info/0/name,additional_info/0/value,additional_info/1/name,additional_info/1/value,salary_info/details/0/grade,salary_info/details/0/payment,salary_info/details/0/amount,salary_info/details/1/next_promotion
1,Peter,PASSPORT,A452817,1-01-2055,Age,19,Gender,M,Manager,Monthly,8956.23,unknown
2,Jane,PASSPORT,B859804,2-01-2035,Age,38,Gender,F,Worker, Monthly,125980.1,unknown
To json output:
[
{
"ID": 1,
"Name": "Peter",
"person_id": {
"id_type": "PASSPORT",
"id_value": "A452817"
},
"person_id_expiry_date": "1-01-2055",
"additional_info": [
{
"name": "Age",
"value": 19
},
{
"name": "Gender",
"value": "M"
}
],
"salary_info": {
"details": [
{
"grade": "Manager",
"payment": "Monthly",
"amount": 8956.23
},
{
"next_promotion": "unknown"
}
]
}
},
{
"ID": 2,
"Name": "Jane",
"person_id": {
"id_type": "PASSPORT",
"id_value": "B859804"
},
"person_id_expiry_date": "2-01-2035",
"additional_info": [
{
"name": "Age",
"value": 38
},
{
"name": "Gender",
"value": "F"
}
],
"salary_info": {
"details": [
{
"grade": "Worker",
"payment": " Monthly",
"amount": 125980.1
},
{
"next_promotion": "unknown"
}
]
}
}
]
Is this something can be done by the existing pandas API or I have to write lots of complex codes to dynamically construct the json object? Thanks.

How to create an automatic mapping of possible JSON data options to be collected?

I've never heard of or found an option for what I'm looking for, but maybe someone knows a way:
To collect the data from a JSON I need to map manually it like this:
events = response['events']
for event in events:
tournament_name = event['tournament']['name']
tournament_slug = event['tournament']['slug']
tournament_category_name = event['tournament']['category']['name']
tournament_category_slug = event['tournament']['category']['slug']
tournament_category_sport_name = event['tournament']['category']['sport']['name']
tournament_category_sport_slug = event['tournament']['category']['sport']['slug']
tournament_category_sport_id = event['tournament']['category']['sport']['id']
The complete model is this:
{
"events": [
{
"tournament": {
"name": "Serie A",
"slug": "serie-a",
"category": {
"name": "Italy",
"slug": "italy",
"sport": {
"name": "Football",
"slug": "football",
"id": 1
},
"id": 31,
"flag": "italy",
"alpha2": "IT"
},
"uniqueTournament": {
"name": "Serie A",
"slug": "serie-a",
"category": {
"name": "Italy",
"slug": "italy",
"sport": {
"name": "Football",
"slug": "football",
"id": 1
},
"id": 31,
"flag": "italy",
"alpha2": "IT"
},
"userCount": 586563,
"id": 23,
"hasEventPlayerStatistics": true
},
"priority": 254,
"id": 33
},
"roundInfo": {
"round": 24
},
"customId": "Kdbsfeb",
"status": {
"code": 7,
"description": "2nd half",
"type": "inprogress"
},
"winnerCode": 0,
"homeTeam": {
"name": "Bologna",
"slug": "bologna",
"shortName": "Bologna",
"gender": "M",
"userCount": 39429,
"nameCode": "BOL",
"national": false,
"type": 0,
"id": 2685,
"subTeams": [
],
"teamColors": {
"primary": "#003366",
"secondary": "#cc0000",
"text": "#cc0000"
}
},
"awayTeam": {
"name": "Empoli",
"slug": "empoli",
"shortName": "Empoli",
"gender": "M",
"userCount": 31469,
"nameCode": "EMP",
"national": false,
"type": 0,
"id": 2705,
"subTeams": [
],
"teamColors": {
"primary": "#0d5696",
"secondary": "#ffffff",
"text": "#ffffff"
}
},
"homeScore": {
"current": 0,
"display": 0,
"period1": 0
},
"awayScore": {
"current": 0,
"display": 0,
"period1": 0
},
"coverage": 1,
"time": {
"initial": 2700,
"max": 5400,
"extra": 540,
"currentPeriodStartTimestamp": 1644159735
},
"changes": {
"changes": [
"status.code",
"status.description",
"time.currentPeriodStart"
],
"changeTimestamp": 1644159743
},
"hasGlobalHighlights": false,
"hasEventPlayerStatistics": true,
"hasEventPlayerHeatMap": true,
"id": 9645399,
"statusTime": {
"prefix": "",
"initial": 2700,
"max": 5400,
"timestamp": 1644159735,
"extra": 540
},
"startTimestamp": 1644156000,
"slug": "empoli-bologna",
"lastPeriod": "period2",
"finalResultOnly": false
}
]
}
In my example I am collecting 7 values.
But there are 83 possible values to be collected.
In case I want to get all the values options that exist in this JSON, is there any way to make this map sequence automatically to print so I can copy it to the code?
Because manually it takes too long to do and it's very tiring.
And the results of texts like print() in terminal would be something like:
tournament_name = event['tournament']['name']
tournament_slug = event['tournament']['slug']
...
...
...
And so on until delivering the 83 object paths with values to collect...
Then I could copy all the prints and paste into my Python file to retrieve the values or any other way to make the work easier.

If the elements in the events arrays are the same, this code works without errors.
def get_prints(recode: dict):
for key in recode.keys():
if type(recode[key]) == dict:
for sub_print in get_prints(recode[key]):
yield [key] + sub_print
else:
yield [key]
class Automater:
def __init__(self,name: str):
"""
Params:
name: name of json
"""
self.name = name
def get_print(self,*args):
"""
Params:
*args: keys json
"""
return '_'.join(args) + ' = ' + self.name + ''.join([f"['{arg}']" for arg in args])
For example, this code:
dicts = {
'tournament':{
'name':"any name",
'slug':'somthing else',
'sport':{
'name':'sport',
'anotherdict':{
'yes':True
}
}
}
}
list_names = get_prints(dicts)
for name in list_names:
print(auto.get_print(*name))
Gives this output:
tournament_name = event['tournament']['name']
tournament_slug = event['tournament']['slug']
tournament_sport_name = event['tournament']['sport']['name']
tournament_sport_anotherdict_yes = event['tournament']['sport']['anotherdict']['yes']

Ignore specific JSON keys when extracting data in Python

I'm extracting certain keys in several JSON files and then converting it to a CSV in Python. I'm able to define a key list when I run my code and get the information I need.
However, there are certain sub-keys that I want to ignore from the JSON file. For example, if we look at the following snippet:
JSON Sample
[
{
"callId": "abc123",
"errorCode": 0,
"apiVersion": 2,
"statusCode": 200,
"statusReason": "OK",
"time": "2020-12-14T12:00:32.744Z",
"registeredTimestamp": 1417731582000,
"UID": "_guid_abc123==",
"created": "2014-12-04T22:19:42.894Z",
"createdTimestamp": 1417731582000,
"data": {},
"preferences": {},
"emails": {
"verified": [],
"unverified": []
},
"identities": [
{
"provider": "facebook",
"providerUID": "123",
"allowsLogin": true,
"isLoginIdentity": true,
"isExpiredSession": true,
"lastUpdated": "2014-12-04T22:26:37.002Z",
"lastUpdatedTimestamp": 1417731997002,
"oldestDataUpdated": "2014-12-04T22:26:37.002Z",
"oldestDataUpdatedTimestamp": 1417731997002,
"firstName": "John",
"lastName": "Doe",
"nickname": "John Doe",
"profileURL": "https://www.facebook.com/John.Doe",
"age": 50,
"birthDay": 31,
"birthMonth": 12,
"birthYear": 1969,
"city": "City, State",
"education": [
{
"school": "High School Name",
"schoolType": "High School",
"degree": null,
"startYear": 0,
"fieldOfStudy": null,
"endYear": 0
}
],
"educationLevel": "High School",
"favorites": {
"music": [
{
"name": "Music 1",
"id": "123",
"category": "Musician/band"
},
{
"name": "Music 2",
"id": "123",
"category": "Musician/band"
}
],
"movies": [
{
"name": "Movie 1",
"id": "123",
"category": "Movie"
},
{
"name": "Movie 2",
"id": "123",
"category": "Movie"
}
],
"television": [
{
"name": "TV 1",
"id": "123",
"category": "Tv show"
}
]
},
"followersCount": 0,
"gender": "m",
"hometown": "City, State",
"languages": "English",
"likes": [
{
"name": "Like 1",
"id": "123",
"time": "2014-10-31T23:52:53.0000000Z",
"category": "TV",
"timestamp": "1414799573"
},
{
"name": "Like 2",
"id": "123",
"time": "2014-09-16T08:11:35.0000000Z",
"category": "Music",
"timestamp": "1410855095"
}
],
"locale": "en_US",
"name": "John Doe",
"photoURL": "https://graph.facebook.com/123/picture?type=large",
"timezone": "-8",
"thumbnailURL": "https://graph.facebook.com/123/picture?type=square",
"username": "john.doe",
"verified": "true",
"work": [
{
"companyID": null,
"isCurrent": null,
"endDate": null,
"company": "Company Name",
"industry": null,
"title": "Company Title",
"companySize": null,
"startDate": "2010-12-31T00:00:00"
}
]
}
],
"isActive": true,
"isLockedOut": false,
"isRegistered": true,
"isVerified": false,
"lastLogin": "2014-12-04T22:26:33.002Z",
"lastLoginTimestamp": 1417731993000,
"lastUpdated": "2014-12-04T22:19:42.769Z",
"lastUpdatedTimestamp": 1417731582769,
"loginProvider": "facebook",
"loginIDs": {
"emails": [],
"unverifiedEmails": []
},
"rbaPolicy": {
"riskPolicyLocked": false
},
"oldestDataUpdated": "2014-12-04T22:19:42.894Z",
"oldestDataUpdatedTimestamp": 1417731582894,
"registered": "2014-12-04T22:19:42.956Z",
"regSource": "",
"socialProviders": "facebook"
}
]
I want to extract data from created and identities but ignore identities.favorites and identities.likes as well as their data underneath it.
This is what I have so far, below. I defined the JSON keys that I want to extract in the key_list variable:
Current Code
import json, pandas
from flatten_json import flatten
# Enter the path to the JSON and the filename without appending '.json'
file_path = r'C:\Path\To\file_name'
# Open and load the JSON file
json_list = json.load(open(file_path + '.json', 'r', encoding='utf-8', errors='ignore'))
# Extract data from the defined key names
key_list = ['created', 'identities']
json_list = [{k:d[k] for k in key_list} for d in json_list]
# Flatten and convert to a data frame
json_list_flattened = (flatten(d, '.') for d in json_list)
df = pandas.DataFrame(json_list_flattened)
# Export to CSV in the same directory with the original file name
export_csv = df.to_csv (file_path + r'.csv', sep=',', encoding='utf-8', index=None, header=True)
Similar to the key_list, I suspect that I would make an ignore list and factor that in the json_list for loop that I have? Something like:
key_ignore = ['identities.favorites', 'identities.likes']`
Then utilize the dict.pop() which looks like it will remove the unwanted sub-keys if it matches? Just not sure how to implement that correctly.
Expected Output
As a result, the code should extract data from the defined keys in key_list and ignore the sub keys defined in key_ignore, which is identities.favorites and identities.likes. Then the rest of the code will continue to convert it into a CSV:
created
identities.0.provider
identities.0.providerUID
identities...
2014-12-04T19:23:05.191Z
site
cb8168b0cf734b70ad541f0132763761
...

If the keys are always there, you can use
del d[0]['identities'][0]['likes']
del d[0]['identities'][0]['favorites']
or if you want to remove the columns from the dataframe after reading all the json data in you can use
df.drop(df.filter(regex='identities.0.favorites|identities.0.likes').columns, axis=1, inplace=True)

How to compare multiple keys value in same JSON file using python

I have a sample JSON file:
"client_info": [
{
"Id": "00201",
"Information": {
"Name": "John",
"Age": 12
},
"Address": [
{
"country": USA,
"location": [
{
"ad1": "NY"
},
{
"ad1": "FL"
},
]
}
]
},
{
"Id": "00202",
"Information": {
"Name": "John",
"Age": 13
},
"Address": [
{
"country": CA,
"location": [
{
"ad1": "NY"
},
{
"ad1": "FL"
},
]
}
]
},
{
"Id": "00203",
"Information": {
"Name": "John",
"Age": 13
},
"Address": [
{
"country": CA,
"location": [
{
"ad1": "NY"
}
]
}
]
}
]
Here I need to compare Information.Name ,Location.ad1 together for each entry. For example: ID 00201 - John, NY, FL is equal with ID 00202 but ID 00203 is different as it has only "ad1": "NY" . Basically need to compare as a set.
I can create the CSV file but my problem is to make that matched result set. I tried the below code to create matched result set but wasnot able to populate the set correcrtly:
uniqueNameSet = set()
uniquelocationSet = set()
for i,client in enumerate(json_data["client_info"]):
if client["Information"]['Name'] not in uniqueNameSet :
uniqueNameSet.add(client["Information"]['Name'])
else:
for j in range(len(client["Address"][0]['location'])):
if client["Address"][0]['location'][j]['ad1'] not in uniquelocationSet :
uniquelocationSet.add(client["Address"][0]['location'][j]['ad1'])
else:
duplictae +=1
I want to generate a CSV for the matched data and removed those from the JSON file.
matched.csv
id Name ad1
00201 John NY,FL
00202 John NY,FL
updated Json file:
"client_info": [
{
"Id": "00203",
"Information": {
"Name": "John",
"Age": 13
},
"Address": [
{
"country": CA,
"location": [
{
"ad1": "NY"
}
]
}
]
}
]