Normalize json with related keys

pandas expand json column
python flatten dictionary to dataframe

I have JSON like this:

in_str='''{
  "prices": [
    [1, 10],
    [2, 20],
    [3, 30]    
  ],
  "total_volumes": [
    [1, 100],
    [2, 200],
    [3, 300]
  ]
}'''

And I am trying to produce pandas data frame with 3 columns (id, price, volume):

1 10 100
2 20 200
3 30 300

I've tried to use pandas.read_json(), but this gives me two columns and I don't know where to go next. json_normalize() gives me back only one row with two columns.

import pandas as pd
import json
from pandas.io.json import json_normalize

in_str='''{
  "prices": [
    [1, 10],
    [2, 20],
    [3, 30]    
  ],
  "total_volumes": [
    [1, 10],
    [2, 20],
    [3, 30]
  ]
}'''

df = pd.read_json(in_str)
json_normalize(json.loads(in_str))

We could just read as usual, re-convert, and merge:

df = pd.read_json(in_str)
pd.merge(*[pd.DataFrame(np.array(df[col].to_list()), 
                        columns=['id', col]) for col in df],
         on='id')

Output:

   id  prices  total_volumes
0   1      10            100
1   2      20            200
2   3      30            300

pandas.json_normalize — pandas 1.1.0 documentation, Normalize semi-structured JSON data into a flat table. Parameters 'ignore' : will ignore KeyError if keys listed in meta are not always present. 'raise' : will raise� result = json_normalize(data, 'values',['key', 'second'],errors='ignore') Real world example with pandas normalization. JSON is very popular format which can be found on many different places. Lets check the Youtube API and request for playlist. The result is in JSON format as you may expect. This is the API request:


pd.read_json doesn't have a suitable orient parameter for this structure. It looks like it is easier to transform json and construct dataframe directly:

>>> pd.DataFrame({key: dict(value) for key, value in json.loads(in_str).items()})

   prices  total_volumes
1      10            100
2      20            200
3      30            300

JSON responses with root keys � Issue #55 � paularmstrong , JSON responses with root keys #55. Closed If we were to take the two examples given above and normalize them with this core schema, we'd get these two outputs: { "entities": Please open a new issue for related bugs. json-api-normalizer. Utility to normalize JSON API data for redux applications. Description. json-api-normalizer helps awesome JSON API and redux work together. Unlike normalizr json-api-normalizer supports JSON API specification, which means that you don't have to care about schemes. It also converts collections into maps, which is a lot more


You may pre-process json to appropriate dictionary and use it to construct the dataframe

import ast

d = ast.literal_eval(in_str)
d1 = {k: dict(v) for k, v in d.items()}
df = pd.DataFrame(d1).rename_axis('id').reset_index()

Out[857]:
   id  prices  total_volumes
0   1      10            100
1   2      20            200
2   3      30            300

Configurable json_normalize with respect to number of levels and , Enhanced json normalize #23861. Closed. 3 of 3 tasks Ignore flattening certain keys in `json_normalize` #27241. Open. weiji14 added a� The following are 40 code examples for showing how to use pandas.io.json.json_normalize().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.


You don't necessarily need to go download pandas if you're not already using it. Here's a solution that uses the builtin json parser to read the file, and native data structures to process the data into your desired shape (and maybe more useful shapes as well).

import json

in_str='''{
  "prices": [
    [1, 10],
    [2, 20],
    [3, 30]    
  ],
  "total_volumes": [
    [1, 100],
    [2, 200],
    [3, 300]
  ]
}'''

in_json = json.loads(in_str)
# you can use json.load(filename) if you're using a file here.
print(in_json)
'''
>>> {'prices': [[1, 10], [2, 20], [3, 30]], 'total_volumes': [[1, 100], [2, 200], [3, 300]]}
'''
# Here we're going to merge the two data sets to make them iterable in one go.
inventory = dict()
for item_id, price in in_json["prices"]:
  inventory[item_id] = {"price": price}

for item_id, volume in in_json["total_volumes"]:
  if isinstance(inventory.get(item_id), dict):
    inventory[item_id]["volume"] = volume
  else:
    inventory[item_id] = {"volume": volume}
print(inventory)
'''
>>> {1: {'price': 10, 'volume': 100}, 2: {'price': 20, 'volume': 200}, 3: {'price': 30, 'volume': 300}}
'''

# Now that the data is all in one dict, we can just iterate through it to get the rows in the shape that you want.
inventory_table = list()
for item_id, info in inventory.items():
  row = [item_id, info.get("price"), info.get("volume")]
  print(row)
  '''
  >>> [1, 10, 100]
  >>> [2, 20, 200]
  >>> [3, 30, 300]
  '''
  inventory_table.append(row)

# the final form
print(inventory_table)
'''
>>> [[1, 10, 100], [2, 20, 200], [3, 30, 300]]
'''

Now that we have that as a baseline, we can make some of the one-liners that people drool over in Python:

import json

in_str='''{
  "prices": [
    [1, 10],
    [2, 20],
    [3, 30]    
  ],
  "total_volumes": [
    [1, 100],
    [2, 200],
    [3, 300]
  ]
}'''

in_json = json.loads(in_str)

inventory = {item_id: {"price": price} for item_id, price in in_json["prices"]}

for item_id, volume in in_json["total_volumes"]:
  if isinstance(inventory.get(item_id), dict):
    inventory[item_id]["volume"] = volume
  else:
    inventory[item_id] = {"volume": volume}

print(inventory)

inventory_table = [[item_id, info.get("price"), info.get("volume")] for item_id, info in inventory.items()]
print(inventory_table)
'''
>>> [[1, 10, 100], [2, 20, 200], [3, 30, 300]]
'''

MySQL 8.0 Reference Manual :: 11.5 The JSON Data Type, Normalization, Merging, and Autowrapping of JSON Values � Searching and Nesting is permitted within JSON array elements and JSON object key values: [ 99, {"id": Relative addressing of array elements is also supported. If path does not� The following are 11 code examples for showing how to use pandas.json_normalize().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.


[PDF] Automatic Generation of Normalized Relational , ABSTRACT. Self-describing key-value data formats such as JSON are be- ing of a set of related, denormalized, semistructured records. The first phase of the� That's it! I hope you find that way of designing JSON data useful. Feel free to comment with your own tips! Last minute addition : Kévin Dunglas, a compatriot strongly involved in PHP/REST communities, cited the following standards a replacement for the structure I showed here: JSON-LD : an alternative to using JSON Reference. What make me


Serializers � Ember CLI Mirage, mirage/serializers/application.js import { JSONAPISerializer } from The normalize method allows you to transform your request body into a JSON:API document, which lets Use this hook to format the key for collections related to this model. json-api-normalizer. Utility to normalize JSON API data for redux applications. Description. json-api-normalizer helps awesome JSON API and redux work together. Unlike normalizr json-api-normalizer supports JSON API specification, which means that you don't have to care about schemes. It also converts collections into maps, which is a lot more


Using jq to “normalize” JSON - json - html, I am trying to use jq to "normalize" JSON so that given the input: [ [ {"M":6, "C":66, " R":0.1}, {"M":6, "C":81, "R":0.9} ], [ {"M":11, "C":94, "R":0.8}, {"M":11, Related. How to insert a key-value pair after a specified key in a JSON object using jq? Normalizes nested JSON according to a schema. Contribute to paularmstrong/normalizr development by creating an account on GitHub.