Playing with a Stock API: A JavaScript/React Developer Learns Python

As a newbie to Python, I recently embarked on a journey to explore its capabilities and use existing APIs. Along the way, I’ve learned how to:

• Set up a virtual environment
• management pack
• Use environment variables with sensitive data
• Make HTTP requests and process JSON
• Implement error handling and string formatting
• Using Python dictionaries
• Understanding the patterns provided by the API Here is a breakdown of my learning experience and key takeaways!

1. Set up Python

python virtual environment (venv) allows you to isolate your project’s dependencies, ensuring that your package does not conflict with other packages. This is very helpful for career development.

Steps to create and start a virtual environment: # Create a virtual environment

python -m venv venv

sourcevenv/bin/activate

venv\script\activate

This keeps your project’s packages separate from other projects’ packages.

Using Python’s package installer pip, I learned to manage dependencies:

pip installation request python-dotenv

pip freeze > requirements.txt

pip install -r requirements.txt

1. Environment Variables To ensure the security of sensitive data, I use .env files as API keys and credentials:

SCHWAB_CLIENT_ID=my_secret_id

SCHWAB_CLIENT_SECRET=my_secret_key

import load_dotenv from dotenv

Import operating system

load_dotenv() # Load variables from .env

api_key = os.getenv(‘SCHWAB_CLIENT_ID’)

Important: Never commit .env files to Git. Use a .gitignore file to exclude them.

1. To make HTTP requests I use the requests library to interact with the API:

import request

response = requests.get(url, headers=headers, params=params)

If response.status_code == 200:

data = response.json() # Convert response to JSON

1. I explored the architecture of the API before interacting with it. API patterns are like blueprints that tell you:

• Request schema: What data you need to send, including required fields, data types and constraints.
• response pattern: What data you expect to receive, including structure, data types and examples. For example, if the API endpoint retrieves stock prices, the schema might look like this:

Request schema:

{

“symbol”: “string”,

“Date”: “String (YYYY-MM-DD)”,

“interval”: “String (for example, ‘1d’, ‘1m’)”

}

response pattern:

{

“symbol”: “string”,

“price”: [

{

“date”: “string (YYYY-MM-DD)”,

“open”: “float”,

“close”: “float”,

“high”: “float”,

“low”: “float”,

“volume”: “integer”

}

]

}

Understanding architecture has two benefits:

1. Prepare: It ensures that you build the request correctly and know how to handle the response.

2. Error prevention: Complying with patterns minimizes invalid requests or misinterpreted responses.
   Patterns save me time and make debugging easier when using the API.

3. Using JSONAPI usually returns data in JSON format. Here’s how I do it in Python:

Import json

Open(‘tokens.json’, ‘r’) as f:

data = json.load(f)

Open(‘tokens.json’, ‘w’) as f:

json.dump(data, f, indentation=4)

1. Error Handling Python’s try/except blocks help me manage errors gracefully:

try:

response = requests.get(url)

data = response.json()

Except exception e:

print(f “Error: {str(e)}”)

Return None

1. Python string formatting F string The .format() method makes string formatting easy:

print(f”Inventory: {name}, Price: ${price:.2f}”)

print(“Inventory: {}, price: ${:.2f}”.format(name, price))

1. Dictionary operations in Python Dictionaries are very powerful for working with nested API data:

Price = data.get(‘price’, ‘not applicable’)

Inventory = data[symbol]

Quote = stock.get(‘Quote’, {})

Price = quote.get(‘lastPrice’, ‘N/A’)

1. Security Best Practices
2. Use .gitignore files to exclude sensitive files.
3. Store the credentials in a .env file.
4. Never hardcode sensitive information.
5. Use template files to share configurations.
6. Debugging Tips Debugging in Python is simple and effective:

print(f”Debug: {variable}”)

print(f”Type: {Type (data)}”)

Import json

print(json.dumps(data, indent=2))

11. Overcoming identity verification challenges

One of the biggest obstacles I faced was working with authentication. I’m stuck for a few days and tried different approaches without success. Eventually I decided to seek support to understand why it wasn’t working.

Turns out the issue was related to the type of account I was using. In order to authenticate successfully I need brokerage account and a Developer account. I initially assumed only a developer account was required, but the API also requires credentials from an active brokerage account.

This experience taught me an important lesson: Don’t hesitate to ask for help when you need it. By putting my ego aside and seeking guidance, I gained a deeper understanding of the problem and solved it faster than if I had continued to struggle on my own

Conclusion Python is very beginner-friendly! Here’s what I learned:

• Virtual environments keep projects organized.
• Environmental variables protect sensitive information.
• Libraries such as requests simplify API calls.
• Good error handling is crucial.
• Clear function names and annotations enhance readability. Next step
• Dive deeper into API authentication.
• Explore data visualization.
• Add more powerful error handling. Final Thoughts The best way to learn is by doing. Don’t be afraid to try and make mistakes – every challenge is an opportunity to grow!

Charles Schwab Account: https://www.schwab.com/brokerage
Charles Schwab Developer Account: https://developer.schwab.com/

Originally published in medium