Working with APIs in Python

APIs (Application Programming Interfaces) allow programs to communicate with other software systems over the internet.

Many modern services provide APIs so developers can access data programmatically.

Examples include:

• Weather data services
• Social media platforms
• Mapping services
• Scientific databases
• Government open data portals

Using Python, you can request data from APIs and analyze it in your own applications.

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What is an API?

An API is a set of rules that allows one application to request information from another.

A typical API request works like this:

1. Your program sends a request to an API server.
2. The server processes the request.
3. The server returns a response, often in JSON format.

Example API response:

{
  "city": "Denver",
  "temperature": 72,
  "condition": "Sunny"
}

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Installing Required Libraries

Most Python API work uses the requests library.

Install it with pip:

pip install requests

Import the library:

import requests

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Making Your First API Request

Example request to a simple public API:

import requests

url = "https://api.example.com/data"

response = requests.get(url)

print(response.status_code)
print(response.text)

Explanation:

• requests.get() sends a request to the API
• status_code shows whether the request succeeded
• response.text contains the returned data

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Understanding Status Codes

APIs return HTTP status codes to indicate success or failure.

Code	Meaning
200	Request successful
400	Bad request
401	Unauthorized
404	Resource not found
500	Server error

Example check:

if response.status_code == 200:
    print("Request successful")
else:
    print("Request failed")

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Working with JSON Data

Most APIs return JSON (JavaScript Object Notation).

Convert a response to Python data:

data = response.json()

print(data)

Example JSON:

{
  "name": "Alice",
  "age": 30
}

Access values:

print(data["name"])

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Example: Requesting Weather Data

Example using a hypothetical weather API.

import requests

url = "https://api.weatherapi.com/v1/current.json"
params = {
    "key": "YOUR_API_KEY",
    "q": "Denver"
}

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

data = response.json()

print(data)

Using parameters allows you to customize API requests.

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Parsing API Data

Once JSON data is returned, you can extract specific fields.

Example:

temperature = data["current"]["temp_f"]
condition = data["current"]["condition"]["text"]

print("Temperature:", temperature)
print("Condition:", condition)

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Converting API Data to a DataFrame

API data can easily be analyzed using pandas.

import pandas as pd

data = response.json()

df = pd.DataFrame(data["results"])

print(df.head())

This allows you to:

• analyze data
• filter results
• visualize trends

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Sending Query Parameters

APIs often require query parameters.

Example:

params = {
    "city": "Denver",
    "units": "metric"
}

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

This generates a request like:

https://api.example.com/data?city=Denver&units=metric

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Authentication with API Keys

Many APIs require authentication.

An API key identifies your application.

Example:

params = {
    "apikey": "YOUR_API_KEY"
}

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

Always keep API keys private and avoid committing them to GitHub.

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Handling Errors

Good programs handle API failures gracefully.

Example:

import requests

try:
    response = requests.get(url)

    response.raise_for_status()

    data = response.json()

except requests.exceptions.RequestException as e:
    print("API request failed:", e)

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Saving API Data

After retrieving data, you may want to save it.

Save as JSON:

import json

with open("data.json", "w") as file:
    json.dump(data, file)

Save as CSV:

import pandas as pd

df = pd.DataFrame(data["results"])
df.to_csv("api_data.csv", index=False)

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Example API Workflow

A typical workflow might look like this:

import requests
import pandas as pd

url = "https://api.example.com/data"

response = requests.get(url)

if response.status_code == 200:

    data = response.json()

    df = pd.DataFrame(data["results"])

    print(df.head())

    df.to_csv("results.csv", index=False)

else:
    print("Request failed:", response.status_code)

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API Best Practices

When working with APIs:

• Always check status codes
• Handle request errors
• Respect API rate limits
• Store API keys securely
• Cache results when possible

These practices help ensure reliable and responsible API usage.

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Common Data Formats Returned by APIs

APIs typically return data in one of these formats:

Format	Description
JSON	Most common format
XML	Structured markup format
CSV	Tabular data
GeoJSON	Geographic data format

Python can easily process all of these formats.

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Summary

APIs allow Python programs to access data from external services.

Using Python you can:

• Send requests to APIs
• Retrieve JSON data
• Convert results to Python objects
• Analyze the data using pandas
• Save results for later use

APIs make it possible to build applications that integrate with online services, databases, and real-time data sources.