Establishing an API connection with the requests library

API Connection

Situation: You need to use requests to consume an API, but with authorization parameters.

Note: This pattern can be used across project, I used this to consume Coordinape API data. You’ll want to set up a dot env file to contain any secret API or authorization keys.

Referencing this stack overflow.

# ---- generic pattern

# Request
REQ = 'https://api.site.com/something/else/else'

# Token (stored in .env)
AUTH_TOKEN = 'Bearer 8888|fliuzabuvdgfnsuczkncsq12454632'

# Use os library to pull Token from .env
auth_token = os.environ.get('AUTH_TOKEN')

# Create Header parameter
HEADER = {'Authorization': f'{auth_token}'}

# Putting it all together
response = requests.get(REQ, headers=HEADER)

# Print result
print(response.json())

Here’s a real example:

# request library to establish connection
import requests
from pprint import pprint

# os library used with python-dotenv to grab AUTH_TOKEN from .env
import os

# using python-dotenv method
from dotenv import load_dotenv
load_dotenv()

# authorization & header
auth_token = os.environ.get('AUTH_TOKEN')
HEADER = {'Authorization': f'{auth_token}'}

# ----- Data Endpoints

# Coordinape Round -- OG Bankless(June): Circle id 24
response24 = requests.get(
    'https://api.coordinape.com/api/v2/token-gifts?circle_id=24', headers=HEADER)
pprint(response24.json())

Navigating Nested JSON Data

import requests
from pprint import pprint
import os
import pandas as pd

# using python-dotenv method
from dotenv import load_dotenv
load_dotenv()

# api endpoints, authorization & header
auth_token = os.environ.get('AUTH_TOKEN')
api_endpoint_manifest = os.environ.get('API_ENDPOINT_MANIFEST')
api_endpoint_users = os.environ.get('API_ENDPOINT_USERS')
HEADER = {'Authorization': f'{auth_token}'}

# --- Primary steps to explore nested data ------
response = requests.get(f'{api_endpoint_manifest}', headers=HEADER)
result = response.json()
dataframe = pd.json_normalize(result)
list_of_column_names = list(dataframe)

# dataframe is [1 rows x 48 columns]
# unpack dataframe using f-string
for item in list_of_column_names:
    print(dataframe[f'{item}'])

# ------------------------------------------------

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