I am new to converting pandas dataframe into json object.
I have a data frame:
Expected json output after conversion is this.
{
"Name": {
"id": "Max",
},
"Favorites" : [
{
"id":"Apple",
"priority":"High",
"Count":"4"
},
{
"id":"Oranges",
"priority":"Medium",
"Count":"2"
},
{
"id":"Banana",
"priority":"Low",
"Count":"1"
}
]
}
Here's a freebie. Hope it helps you learn how to write it yourself in the future :)
output = []
for index, row in df.iterrows():
entry = {
"Name": {
"id": row['Names']
},
"Favorites": [
{
"id": row['High_Priority_Goods_Name'],
"priority": "High",
"count": row['High_Priority_Goods_Count']
},
{
"id": row['Medium_Priority_Goods_Name'],
"priority": "Medium",
"count": row['Medium_Priority_Goods_Count']
},
{
"id": row['Low_Priority_Goods_Name'],
"priority": "Low",
"count": row['Low_Priority_Goods_Count']
}
]
}
output.append(entry)
print(output)
Related
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.
Converted into a nested JSON file using Pandas
This is the sample csv for one row
name type aitm alitm aaitm adsc1
specs glass 70072187 ESA65Z45 ESA 65Z45 CUT TIP FG 1808-40
I'm trying to achieve the below structure of Nested JSON for every row
import pandas as pd
import json
df = pd.DataFrame([['specs','glass','70072187','ESA65Z45','ESA 65Z45','CUT TIP FG 1808-40'],
['specs','glass','666','ESA6665','ESB 666','CUT TIP FG 66-40']],
columns = ['name', 'type','aitm','alitm','aaitm','adsc1' ])
data = {'entities':[]}
for key,grp in df.groupby('name'):
for idx, row in grp.iterrows():
temp_dict_alpha = {'name':key, 'type':row['type'], 'data':{'attributes':{}}}
attr_row = row[~row.index.isin(['name','type'])]
for idx2, row2 in attr_row.iteritems():
dict_temp = {}
dict_temp[idx2] = {'values':[]}
dict_temp[idx2]['values'].append({'value':row2,'source':'internal','locale':'en_US'})
temp_dict_alpha['data']['attributes'].update(dict_temp)
data['entities'].append(temp_dict_alpha)
print(json.dumps(data, indent= 4))
Output:
print(json.dumps(data, indent= 4))
{
"entities": [
{
"name": "specs",
"type": "glass",
"data": {
"attributes": {
"aitm": {
"values": [
{
"value": "70072187",
"source": "internal",
"locale": "en_US"
}
]
},
"alitm": {
"values": [
{
"value": "ESA65Z45",
"source": "internal",
"locale": "en_US"
}
]
},
"aaitm": {
"values": [
{
"value": "ESA 65Z45",
"source": "internal",
"locale": "en_US"
}
]
},
"adsc1": {
"values": [
{
"value": "CUT TIP FG 1808-40",
"source": "internal",
"locale": "en_US"
}
]
}
}
}
},
{
"name": "specs",
"type": "glass",
"data": {
"attributes": {
"aitm": {
"values": [
{
"value": "666",
"source": "internal",
"locale": "en_US"
}
]
},
"alitm": {
"values": [
{
"value": "ESA6665",
"source": "internal",
"locale": "en_US"
}
]
},
"aaitm": {
"values": [
{
"value": "ESB 666",
"source": "internal",
"locale": "en_US"
}
]
},
"adsc1": {
"values": [
{
"value": "CUT TIP FG 66-40",
"source": "internal",
"locale": "en_US"
}
]
}
}
}
}
]
}
I have a csv file with 4 columns data as below.
type,MetalType,Date,Acknowledge
Metal,abc123451,2018-05-26,Success
Metal,abc123452,2018-05-27,Success
Metal,abc123454,2018-05-28,Failure
Iron,abc123455,2018-05-29,Success
Iron,abc123456,2018-05-30,Failure
( I just provided header in the above example data but in my case i dont have header in the data)
how can i convert above csv file to Json in the below format...
1st Column : belongs to --> "type": "Metal"
2nd Column : MetalType: "values" : "value": "abc123451"
3rd column : "Date": "values":"value": "2018-05-26"
4th Column : "Acknowledge": "values":"value": "Success"
and remaining all columns are default values.
As per below format ,
{
"entities": [
{
"id": "XXXXXXX",
"type": "Metal",
"data": {
"attributes": {
"MetalType": {
"values": [
{
"source": "XYZ",
"locale": "Australia",
"value": "abc123451"
}
]
},
"Date": {
"values": [
{
"source": "XYZ",
"locale": "Australia",
"value": "2018-05-26"
}
]
},
"Acknowledge": {
"values": [
{
"source": "XYZ",
"locale": "Australia",
"value": "Success"
}
]
}
}
}
}
]
}
Even though jww is right, I built something for you:
I import the csv using pandas:
df = pd.read_csv('data.csv')
then I create a template for the dictionaries you want to add:
d_json = {"entities": []}
template = {
"id": "XXXXXXX",
"type": "",
"data": {
"attributes": {
"MetalType": {
"values": [
{
"source": "XYZ",
"locale": "Australia",
"value": ""
}
]
},
"Date": {
"values": [
{
"source": "XYZ",
"locale": "Australia",
"value": ""
}
]
},
"Acknowledge": {
"values": [
{
"source": "XYZ",
"locale": "Australia",
"value": ""
}
]
}
}
}
}
Now you just need to fill in the dictionary:
for i in range(len(df)):
d = template
d['type'] = df['type'][i]
d['data']['attributes']['MetalType']['values'][0]['value'] = df['MetalType'][i]
d['data']['attributes']['Date']['values'][0]['value'] = df['Date'][i]
d['data']['attributes']['Acknowledge']['values'][0]['value'] = df['Acknowledge'][i]
d_json['entities'].append(d)
I know my way of iterating over the df is kind of ugly, maybe someone knows a cleaner way.
Cheers!
I have rather very weird requirement now. I have below json and somehow I have to convert it into flat csv.
[
{
"authorizationQualifier": "SDA",
"authorizationInformation": " ",
"securityQualifier": "ASD",
"securityInformation": " ",
"senderQualifier": "ASDAD",
"senderId": "FADA ",
"receiverQualifier": "ADSAS",
"receiverId": "ADAD ",
"date": "140101",
"time": "0730",
"standardsId": null,
"version": "00501",
"interchangeControlNumber": "123456789",
"acknowledgmentRequested": "0",
"testIndicator": "T",
"functionalGroups": [
{
"functionalIdentifierCode": "ADSAD",
"applicationSenderCode": "ASDAD",
"applicationReceiverCode": "ADSADS",
"date": "20140101",
"time": "07294900",
"groupControlNumber": "123456789",
"responsibleAgencyCode": "X",
"version": "005010X221A1",
"transactions": [
{
"name": "ASDADAD",
"transactionSetIdentifierCode": "adADS",
"transactionSetControlNumber": "123456789",
"implementationConventionReference": null,
"segments": [
{
"BPR03": "ad",
"BPR14": "QWQWDQ",
"BPR02": "1.57",
"BPR13": "23223",
"BPR01": "sad",
"BPR12": "56",
"BPR10": "32424",
"BPR09": "12313",
"BPR08": "DA",
"BPR07": "123456789",
"BPR06": "12313",
"BPR05": "ASDADSAD",
"BPR16": "21313",
"BPR04": "SDADSAS",
"BPR15": "11212",
"id": "aDSASD"
},
{
"TRN02": "2424",
"TRN03": "35435345",
"TRN01": "3435345",
"id": "FSDF"
},
{
"REF02": "fdsffs",
"REF01": "sfsfs",
"id": "fsfdsfd"
},
{
"DTM02": "2432424",
"id": "sfsfd",
"DTM01": "234243"
}
],
"loops": [
{
"id": "24324234234",
"segments": [
{
"N101": "sfsfsdf",
"N102": "sfsf",
"id": "dgfdgf"
},
{
"N301": "sfdssfdsfsf",
"N302": "effdssf",
"id": "fdssf"
},
{
"N401": "sdffssf",
"id": "sfds",
"N402": "sfdsf",
"N403": "23424"
},
{
"PER06": "Wsfsfdsfsf",
"PER05": "sfsf",
"PER04": "23424",
"PER03": "fdfbvcb",
"PER02": "Pedsdsf",
"PER01": "sfsfsf",
"id": "fdsdf"
}
]
},
{
"id": "2342",
"segments": [
{
"N101": "sdfsfds",
"N102": "vcbvcb",
"N103": "dsfsdfs",
"N104": "343443",
"id": "fdgfdg"
},
{
"N401": "dfsgdfg",
"id": "dfgdgdf",
"N402": "dgdgdg",
"N403": "234244"
},
{
"REF02": "23423342",
"REF01": "fsdfs",
"id": "sfdsfds"
}
]
}
]
}
]
}
]
}
]
The column header name corresponding to deeper key-value make take nested form, like functionalGroups[0].transactions[0].segments[0].BPR15.
I am able to do this in java using this github project (here you can find the output format I desire in the explanation) in one line:
flatJson = JSONFlattener.parseJson(new File("files/simple.json"), "UTF-8");
The output was:
date,securityQualifier,testIndicator,functionalGroups[1].functionalIdentifierCode,functionalGroups[1].date,functionalGroups[1].applicationReceiverCode, ...
140101,00,T,HP,20140101,ETIN,...
But I want to do this in python. I tried as suggested in this answer:
with open('data.json') as data_file:
data = json.load(data_file)
df = json_normalize(data, record_prefix=True)
with open('temp2.csv', "w", newline='\n') as csv_file:
csv_file.write(df.to_csv())
However, for column functionalGroups, it dumps json as a cell value.
I also tried as suggested in this answer:
with open('data.json') as f: # this ensures opening and closing file
a = json.loads(f.read())
df = pandas.DataFrame(a)
print(df.transpose())
But this also seem to do the same:
0
acknowledgmentRequested 0
authorizationInformation
authorizationQualifier SDA
date 140101
functionalGroups [{'functionalIdentifierCode': 'ADSAD', 'applic...
interchangeControlNumber 123456789
receiverId ADAD
receiverQualifier ADSAS
securityInformation
securityQualifier ASD
senderId FADA
senderQualifier ASDAD
standardsId None
testIndicator T
time 0730
version 00501
Is it possible to do what I desire in python?
I have a dictionary which is contain multiple keys and values and the values also contain the key, value pair. I am not getting how to create dynamic json using this dictionary in python. Here's the dictionary:
image_dict = {"IMAGE_1":{"img0":"IMAGE_2","img1":"IMAGE_3","img2":"IMAGE_4"},"IMAGE_2":{"img0":"IMAGE_1", "img1" : "IMAGE_3"},"IMAGE_3":{"img0":"IMAGE_1", "img1":"IMAGE_2"},"IMAGE_4":{"img0":"IMAGE_1"}}
My expected result like this :
{
"data": [
{
"image": {
"imageId": {
"id": "IMAGE_1"
},
"link": {
"target": {
"id": "IMAGE_2"
},
"target": {
"id": "IMAGE_3"
},
"target": {
"id": "IMAGE_4"
}
}
},
"updateData": "link"
},
{
"image": {
"imageId": {
"id": "IMAGE_2"
},
"link": {
"target": {
"id": "IMAGE_1"
},
"target": {
"id": "IMAGE_3"
}
}
},
"updateData": "link"
},
{
"image": {
"imageId": {
"id": "IMAGE_3"
},
"link": {
"target": {
"id": "IMAGE_1"
},
"target": {
"id": "IMAGE_2"
}
}
},
"updateData": "link"
} ,
{
"image": {
"imageId": {
"id": "IMAGE_4"
},
"link": {
"target": {
"id": "IMAGE_1"
}
}
},
"updateData": "link"
}
]
}
I tried to solve it but I didn't get expected result.
result = {"data":[]}
for k,v in sorted(image_dict.items()):
for a in sorted(v.values()):
result["data"].append({"image":{"imageId":{"id": k},
"link":{"target":{"id": a}}},"updateData": "link"})
print(json.dumps(result, indent=4))
In Python dictionaries you can't have 2 values with the same key. So you can't have multiple targets all called "target". So you can index them. Also I don't know what this question has to do with dynamic objects but here's the code I got working:
import re
dict_res = {}
ind = 0
for image in image_dict:
lin_ind = 0
sub_dict = {'image' + str(ind): {'imageId': {image}, 'link': {}}}
for sub in image_dict[image].values():
sub_dict['image' + str(ind)]['link'].update({'target' + str(lin_ind): {'id': sub}})
lin_ind += 1
dict_res.update(sub_dict)
ind += 1
dict_res = re.sub('target\d', 'target', re.sub('image\d', 'image', str(dict_res)))
print dict_res