What is the best way to separate a JSON response into different sections based on the value of the name field?

Hello, I am currently making an API call to the Myfxbook API and receiving a JSON response with a large amount of data. Is it possible to break down this extensive response into multiple smaller parts based on the 'name' value? Here is a snippet of the JSON response:

[{'name': 'EURUSD', 'shortPercentage': 32, 'longPercentage': 68, ...}, {'name': 'GBPUSD', 'shortPercentage': 54, 'longPercentage': 46, ...}, {...}]

Each currency name ('name') has associated values like short percentage, long percentage, volume, etc.

I am specifically interested in extracting the short percentage and long percentage for certain currencies, rather than all of them.

For example:

EURUSD    32    68

GBPUSD    54    46

USDJPY    .......
:
:
:

I hope this clarifies my query. Thank you.

pythonjsonjsonresponse

Answer №1

Utilize comprehensiveness:

# info = this represents a large input dataset
selected = ['AUDCAD', 'NZDUSD']
outcome = [{'pair' : item['name'],
           'short' : item['shortPercentage'],
           'long' : pair['longPercentage']}
          for item in info if item['name'] in selected]
print(outcome)

Result displayed in condensed form:

[{'pair': 'AUDCAD', 'short': 22, 'long': 78}, {'pair': 'NZDUSD', 'short': 60, 'long': 40}]

Answer №2

Are you looking for this information?

from tabulate import tabulate

data = [{'name': 'EURUSD', 'shortPercentage': 32, 'longPercentage': 68, 'shortVolume': 22685.66, 'longVolume': 47762.79, 'longPositions': 89968, 'shortPositions': 56612, 'totalPositions': 146580, 'avgShortPrice': 1.1678, 'avgLongPrice': 1.199}, {'name': 'GBPUSD', 'shortPercentage': 54, 'longPercentage': 46, 'shortVolume': 6462.95, 'longVolume': 5587.2, 'longPositions': 17783, 'shortPositions': 22240, 'totalPositions': 40023, 'avgShortPrice': 1.3592, 'avgLongPrice': 1.3929}, {'name': 'USDJPY', 'shortPercentage': 89, 'longPercentage': 11, 'shortVolume': 32447.66, 'longVolume': 4081.71, 'longPositions': 10941, 'shortPositions': 58722, 'totalPositions': 69663, 'avgShortPrice': 106.7526, 'avgLongPrice': 108.6437}, {'name': 'GBPJPY', 'shortPercentage': 79, 'longPercentage': 21, 'shortVolume': 2725.22, 'longVolume': 711.99, 'longPositions': 3210, 'shortPositions': 9743, 'totalPositions': 12953, 'avgShortPrice': 146.1479, 'avgLongPrice': 149.2243}, {'name': 'USDCAD', 'shortPercentage': 47, 'longPercentage': 53, 'shortVolume': 6235.58, 'longVolume': 6930.97, 'longPositions': 16121, 'shortPositions': 13276, 'totalPositions': 29397, 'avgShortPrice': 1.2658, 'avgLongPrice': 1.3025}, {'name': 'EURAUD', 'shortPercentage': 32, 'longPercentage': 68, 'shortVolume': 562.13, 'longVolume': 1170.82, 'longPositions': 444...
print(tabulate(parsed, headers=["Name", "Short %", "Long %"], tablefmt="sql"))

Here is the output:

Name      Short %    Long %
------  ---------  --------
EURUSD         32        68
GBPUSD         54        46
USDJPY         89        11
GBPJPY         79        21
USDCAD         47        53
EURAUD         32        68
EURJPY         74        26
AUDCAD         48        52
AUDJPY         83        17
AUDNZD         63        37

Answer №3

Based on my assumption, it appears that this is the information you are seeking.

Here are the results:

l=[{'name': 'EURUSD', 'shortPercentage': 32, 'longPercentage': 68, 'shortVolume': 22685.66, 'longVolume': 47762.79, 'longPositions': 89968, 'shortPositions': 56612, 'totalPositions': 146580, 'avgShortPrice': 1.1678, 'avgLongPrice': 1.199}, {'name': 'GBPUSD', 'shortPercentage': 54, 'longPercentage': 46, 'shortVolume': 6462.95, 'longVolume': 5587.2, 'longPositions': 17783, 'shortPositions': 22240, 'totalPositions': 40023, 'avgShortPrice': 1.3592, 'avgLongPrice': 1.3929}, {'name': 'USDJPY', 'shortPercentage': 89, 'longPercentage': 11, 'shortVolume': 32447.66, 'longVolume': 4081.71, 'longPositions': 10941, 'shortPositions': 58722, 'totalPositions': 69663, 'avgShortPrice': 106.7526, 'avgLongPrice': 108.6437}, {'name': 'GBPJPY', 'shortPercentage': 79, 'longPercentage': 21, 'shortVolume': 2725.22, 'longVolume': 711.99, 'longPositions': 3210, 'shortPositions': 9743, 'totalPositions': 12953, 'avgShortPrice': 146.1479, 'avgLongPrice': 149.2243}, {'name': 'USDCAD', 'shortPercentage': 47, 'longPercentage': 53, 'shortVolume': 6235.58, 'longVolume': 6930.97, 'longPositions': 16121, 'shortPositions': 13276, 'totalPositions': 29397, 'avgShortPrice': 1.2658, 'avgLongPrice': 1.3025}, {'name': 'EURAUD', 'shortPercentage': 32, 'longPercentage': 68, 'shortVolume': 562.13, 'longVolume': 1170.82, 'longPositions': 4444, 'shortPositions': 3004, 'totalPositions': 7448, 'avgShortPrice': 1.5434, 'avgLongPrice': 1.5816}, {'name': 'EURJPY', 'shortPercentage': 74, 'longPercentage': 26, 'shortVolume': 10500.41, 'longVolume': 3672.26, 'longPositions': 8166, 'shortPositions': 25254, 'totalPositions': 33420, 'avgShortPrice': 126.1881, 'avgLongPrice': 128.6925}, {'name': 'AUDCAD', 'shortPercentage': 48, 'longPercentage': 52, 'shortVolume': 1559.83, 'longVolume': 1699.36, 'longPositions': 5943, 'shortPositions': 6278, 'totalPositions': 12221, 'avgShortPrice': 0.9561, 'avgLongPrice': 0.979}, {'name': 'AUDJPY', 'shortPercentage': 83, 'longPercentage': 17, 'shortVolume': 1614.66, 'longVolume': 322.98, 'longPositions': 1905, 'shortPositions': 6048, 'totalPositions': 7953, 'avgShortPrice': 78.9751, 'avgLongPrice': 83.5306}, {'name': 'AUDNZD', 'shortPercentage': 63, 'longPercentage': 37, 'shortVolume': 673.48, 'longVolume': 403.95, 'longPositions': 2123, 'shortPositions': 3785, 'totalPositions': 5908, 'avgShortPrice': 1.0644, 'avgLongPrice': 1.0779}]

for i in range(len(l)):
    print(l[i]["name"],end=" ")
    print(l[i]["shortPercentage"],end=" ")
    print(l[i]["longPercentage"],end=" ")
    print("\n")

Here is the output:

EURUSD 32 68 

GBPUSD 54 46 

USDJPY 89 11 

GBPJPY 79 21 

USDCAD 47 53 

EURAUD 32 68 

EURJPY 74 26 

AUDCAD 48 52 

AUDJPY 83 17 

AUDNZD 63 37

If you would like to focus on specific currencies, you can use conditional statements to only display those of interest.

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