Very simple:
import json
data = json.loads('{"one" : "1", "two" : "2", "three" : "3"}')
print(data['two']) # or `print data['two']` in Python 2
Very simple:
import json
data = json.loads('{"one" : "1", "two" : "2", "three" : "3"}')
print(data['two']) # or `print data['two']` in Python 2
For URL or file, use json.load(). For string with .json content, use json.loads().
#! /usr/bin/python
import json
# from pprint import pprint
json_file = 'my_cube.json'
cube = '1'
with open(json_file) as json_data:
data = json.load(json_data)
# pprint(data)
print "Dimension: ", data['cubes'][cube]['dim']
print "Measures: ", data['cubes'][cube]['meas']
Python Parse JSON array - Stack Overflow
Using Python to Parse a JSON Object
Handling JSON files with ease in Python
How to parse different part of json file (Newbie here)
Videos
In your for loop statement, Each item in json_array is a dictionary and the dictionary does not have a key store_details. So I modified the program a little bit
import json
input_file = open ('stores-small.json')
json_array = json.load(input_file)
store_list = []
for item in json_array:
store_details = {"name":None, "city":None}
store_details['name'] = item['name']
store_details['city'] = item['city']
store_list.append(store_details)
print(store_list)
If you arrived at this question simply looking for a way to read a json file into memory, then use the built-in json module.
with open(file_path, 'r') as f:
data = json.load(f)
If you have a json string in memory that needs to be parsed, use json.loads() instead:
data = json.loads(my_json_string)
Either way, now data is converted into a Python data structure (list/dictionary) that may be (deeply) nested and you'll need Python methods to manipulate it.
If you arrived here looking for ways to get values under several keys as in the OP, then the question is about looping over a Python data structure. For a not-so-deeply-nested data structure, the most readable (and possibly the fastest) way is a list / dict comprehension. For example, for the requirement in the OP, a list comprehension does the job.
store_list = [{'name': item['name'], 'city': item['city']} for item in json_array]
# [{'name': 'Mall of America', 'city': 'Bloomington'}, {'name': 'Tempe Marketplace', 'city': 'Tempe'}]
Other types of common data manipulation:
For a nested list where each sub-list is a list of items in the
json_array.store_list = [[item['name'], item['city']] for item in json_array] # [['Mall of America', 'Bloomington'], ['Tempe Marketplace', 'Tempe']]For a dictionary of lists where each key-value pair is a category-values in the
json_array.store_data = {'name': [], 'city': []} for item in json_array: store_data['name'].append(item['name']) store_data['city'].append(item['city']) # {'name': ['Mall of America', 'Tempe Marketplace'], 'city': ['Bloomington', 'Tempe']}For a "transposed" nested list where each sub-list is a "category" in
json_array.store_list = list(store_data.values()) # [['Mall of America', 'Tempe Marketplace'], ['Bloomington', 'Tempe']]
Being retired, I’ve embarked on a project to keep my brain working as well as keeping my dwindling Python skills from completely disappearing.
I’ve been tinkering around with an API that allows me to grab time sequence weather data from a DOT weather station. As with most APIs the data is returned as a json object which to me (and Python) is a really ugly dictionary embedded with subsequent dictionaries that have keys and associated lists/tuples of values. For example one such dictionary has the AirTemp as the key with quarter hour values for as many hours I scrape. There are as many of these dictionaries as weather variables I choose to download : wind speed,direction, etc etc.
My approach to parsing the json object into something readable is to filter out each of the weather variable data into their own dictionary followed by creating a list of all the values in the data dictionary.
Finally, I zip those lists together and create a dictionary of the date/time variable as the key with the various weather variable values in a list associated to that specific date/time.
I’m just wondering if there might be different approach would be more efficient (and Pythonic). Sometimes I feel like I'm beating data into submission rather than processing it. This is the first time ever scripting to an API and parsing a json object.
import requests,json
weather_url = "https://api.weatherdata.com/v2/stations/timeseries?&token=MyToken&units=temp|f,speed|mph&stid=UTHEB&vars=wind_speed,wind_direction,air_temp&obtimezone=local&start=202603031700&end=202603032200"
response = requests.get(weather_url)
responseStatusCode = (response.status_code)
if responseStatusCode == 200:
print(f"Connected to Weather Data Server; data incoming")
jsonStr = response.json()
tempDict = jsonStr['STATION'][0]#dictionary in json object with data
obsDict = tempDict['OBSERVATIONS']#dictionary in station of data
#begin list creation:
dateList = obsDict['date_time']
localDateList =[i.replace("T"," ").replace(":00-0700","-MT").replace("TimeStamp: ","") for i in dateList]
airTempList = obsDict['air_temp_set_1']
windSpeedList = obsDict['wind_speed_set_1']
windDirectionList = obsDict['wind_direction_set_1']
displayDict= {date: [speed,direction,temp]
for date,speed,direction,temp in zip(localDateList,windSpeedList,windDirectionList,airTempList)}
for key,value in displayDict.items():
print(f"Date-Time: {key} WindSpeed MPH: {value[0]} WindDirection Degrees: {value[1]} AirTemp: {value[2]}")
else:
print(f"Unable to connect to Weather Data Server: try again later")I have finished writing the third article in the Data Engineering with Python series. This is about working with JSON data in Python. I have tried to cover every necessary use case. If you have any other suggestions, let me know.
Working with JSON in Python
Data Engineering with Python series
Hi all,
Long time programmer here, but new to Python. This will be a long and, I think, complicated issue, so appreciate anyone who reads through it all and has any suggestions. I've looked up different ways to pull this data and don't seem to be making any progress. I'm sure there's a much better way.
I'm writing a program that will connect to our library to pull a list of everything we have checked out and I want to output a sorted list by due date and whether it has holds or not. I've got the code working to log in and pull a json data structure, but I cannot get it to export the data in the correct order. The json data is(to me) hideously complex with some data(due date) in one section and other data in another section. I'm able to pull the fields I want, but keeping them together is proving challenging.
For example, the title and subtitle are in the 'bibs/briefinfo' section with a key value of 'title' or 'subtitle'. Due Date is also in the 'checkouts' section with a key value of 'dueDate'. When I loop through them, though, the Titles are in one order, the due dates are in another order and the subtitles another.
I used BeautifulSoup because it's a webpage with json in it, so used BS to read the webpage.
I'm wanting to pull the following fields for each book so I can display the info for each book:
title, subtitle, contentType from briefinfo section
duedate from checkouts section heldcopies and availablecopies from the availability section
Here's the pertinent section of my code:
soup = BeautifulSoup(index_page.text, 'html.parser')
all_scripts = soup.find_all('script', {"type":"application/json"})
for script in all_scripts:
jsondata = json.loads(script.text)
print(jsondata)
output = []
for i in item_generator(jsondata, "bibTitle"):
ans = {i}
print(i)
output.append(ans)
for i in item_generator(jsondata, "dueDate"):
ans = {i}
output.append(ans)
print("Subtitle----------------------")
for i in item_generator(jsondata, "subtitle"):
ans = {i}
print(i)
output.append(ans)
print(output)Here's the json output from my print statement so I can see what I'm working with. I tried to format it so it's easier to read. I removed a lot of other elements to keep the size down. Hopefully I didn't break any of the brackets.
{
'app':
{
'coreCssFingerprint': '123123123',
'coreAssets':
{
'cdnHost': 'https://xyz.com',
'cssPath': '/dynamic_stylesheet',
'defaultStylesheet': 'xyz.css'
},
},
'entities':
{
'listItems': {},
'cards': {},
'accounts':
{
'88888888':
{'barcode': '999999999',
'expiryDate': None,
'id': 88888888,
}
},
'shelves':
{'88888888':
{'1111222222':
{
'id': 1111222222,
'metadataId': 'S00A1122334',
'shelf': 'for_later',
'privateItem': True,
'dateAdded': '2023-12-30',
},
}
},
'users':
{'88888888':
{ 'accounts': \[88888888\],
'status': 'A',
'showGroupingDebug': False,
'avatarUrl': '',
'id': 88888888,
}
},
'eventPrograms': {}, 'checkouts':
{'112233445566778899':
{
'checkoutId': '112233445566778899',
'materialType': 'PHYSICAL',
'dueDate': '2024-08-26',
'metadataId': 'S99Z000000',
'bibTitle': "The Lord of the Rings"
},
'998877665544332211':
{
'checkoutId': 998877665544332211',
'materialType': 'PHYSICAL',
'dueDate': '2024-08-26',
'metadataId': 'S88Y00000',
'bibTitle': 'The Lord of the Rings'
},
},
'eventSeries': {}, 'catalogBibs': {},'bibs':
{'S88Y00000':
{
'id': 'S88Y00000',
'briefInfo':
{
'superFormats': ['BOOKS', 'MODERN_FORMATS'],
'genreForm': [],
'callNumber': '123.456',
'authors': ['Tolkien, J.R.R.'],
'metadataId': 'S88Y00000',
'jacket':
{
'type': 'hardcover',
'local_url': None
},
'contentType': 'FICTION',
'format': 'BK',
'subtitle': 'The Two Towers',
'title': 'The Lord of the Rings',
'id': 'S88Y00000',
},
'availability':
{
'heldCopies': 0,
'singleBranch': False,
'metadataId': 'S88Y00000',
'statusType': 'AVAILABLE',
'totalCopies': 3,
'availableCopies': 2
}
},
'S77X12345':
{
'id': 'S77X12345',
'briefInfo':
{
'superFormats': ['BOOKS', 'MODERN_FORMATS'],
'genreForm': [],
'callNumber': '123.457',
'authors': ['Tolkien, J.R.R.'],
'metadataId': 'S77X12345',
'jacket':
{
'type': 'hardcover',
'local_url': None
},
'contentType': 'FICTION',
'format': 'BK',
'subtitle': 'The Fellowship of the Ring',
'title': 'The Lord of the Rings',
'id': 'S77X12345',
},
'availability':
{
'heldCopies': 0,
'singleBranch': False,
'metadataId': 'S77X12345',
'statusType': 'AVAILABLE',
'totalCopies': 2,
'availableCopies': 1
}
}
Anyone know of a better way to parse this data? Thanks!