SkyLimit Tech Hub: Data Science Training Center

Course 5: Working with APIs and Real-World Datasets

This course dives into working with APIs (Application Programming Interfaces) and real-world datasets, teaching learners how to retrieve, process, and analyze live data using Python. Designed for those familiar with Python, Pandas, and visualization libraries, this course equips you with skills to integrate dynamic data into your workflows, culminating in a capstone project. Over one week, daily lessons build expertise in API requests, JSON parsing, data cleaning, and visualization.

Objective: By the end of the course, students will be able to fetch data from public APIs, clean and transform it with Pandas, create insightful visualizations, and complete a full data analysis project, preparing them for real-world data science tasks.

Scope: The course covers API fundamentals, JSON handling, real-world API integration, combining APIs with Pandas and visualization, and a capstone project, providing a complete data pipeline from external sources to actionable insights.

Day 1: Introduction to APIs

Introduction: APIs (Application Programming Interfaces) are gateways to real-time data from external services, empowering analysts and developers to integrate dynamic, up-to-date information into their workflows.

Learning Objective: By the end of this lesson, learners will be able to explain what an API is, make basic API requests using Python’s requests library, and retrieve and parse JSON data for analysis.

Scope of the Lesson: This session covers core API concepts, making HTTP GET requests using requests.get(), handling server responses, and parsing JSON responses to extract meaningful data.

Background Information: APIs act as intermediaries between applications, offering structured access to external data sources (e.g., weather forecasts, stock prices, social media feeds). Python’s requests library simplifies interaction with APIs, allowing users to send HTTP requests and receive responses. Typical workflow: Send a GET request: requests.get(url); Check the response status code: response.status_code; Parse JSON data: response.json(). Many APIs require an API key for authentication and may enforce rate limits. Reading API documentation carefully is crucial for successful interaction.

Examples to Practice:

import requests

# Example: Fetching data from a sample API
url = 'https://api.example.com/data'
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    data = response.json()  # Parse the JSON response
    print(data)
else:
    print("Failed to retrieve data:", response.status_code)

Explanation of the Example Code: This example demonstrates how to request data from an API endpoint. It checks the server's response status (200 means success), and if successful, parses the JSON content into a Python dictionary or list for easy data manipulation.

Supplemental Information: APIs are foundational tools for real-world projects where manual data downloads are impractical. Whether pulling weather reports, financial data, or social media metrics, mastering API requests unlocks a world of real-time, automated analysis opportunities.

Resources:

Day 2: Working with JSON Data

Introduction: JSON (JavaScript Object Notation) is the most common format for API responses, offering a structured, lightweight way to transmit data. Python’s built-in tools make parsing and processing JSON straightforward for analysis.

Learning Objective: By the end of this lesson, learners will be able to parse JSON responses, navigate nested structures, convert JSON into Pandas DataFrames, and implement basic error handling.

Scope of the Lesson: This session covers understanding JSON structures, accessing and manipulating nested JSON data in Python, converting structured JSON into DataFrames for analysis, and handling potential parsing errors.

Background Information: JSON organizes data using key-value pairs (like Python dictionaries) and arrays (like lists). When working with APIs: Use response.json() to parse JSON into Python structures; Access nested data using keys, e.g., data['key']['subkey']; Flatten and convert JSON structures into DataFrames using pandas.DataFrame() for easier analysis. Proper error handling is critical to gracefully manage malformed data or unexpected API responses, ensuring your code remains robust and reliable.

Examples to Practice:

import requests
import pandas as pd

# Example: Parsing JSON and converting to a DataFrame
url = 'https://api.example.com/data'
response = requests.get(url)

if response.status_code == 200:
    json_data = response.json()
    # Accessing nested elements
    nested_value = json_data['main_key']['sub_key']
    
    # Convert a list of records to a DataFrame
    if isinstance(json_data['records'], list):
        df = pd.DataFrame(json_data['records'])
        print(df.head())
else:
    print("Error fetching data:", response.status_code)

Explanation of the Example Code: The API response is parsed into a dictionary. Nested keys are accessed by chaining brackets. If the JSON contains a list of records, it is directly loaded into a Pandas DataFrame for structured analysis. The response status code is checked to handle potential errors.

Supplemental Information: Understanding and manipulating JSON is crucial for real-world data projects, as APIs often return deeply nested or complex structures. Developing fluency in JSON handling allows seamless integration of external data sources into Python workflows.

Resources:

Day 3: Real-World API Examples

Introduction: Leveraging public APIs like weather, news, and finance services provides practical experience in retrieving, processing, and analyzing live, real-world data.

Learning Objective: By the end of this session, learners will be able to select appropriate APIs, make authenticated API requests, parse and store API responses, and respect usage policies.

Scope of the Lesson: This lesson covers: Identifying useful public APIs; Setting up authenticated requests (using API keys); Passing parameters to customize queries; Parsing responses into usable formats (e.g., DataFrames); Saving or exporting retrieved data for analysis.

Background Information: Public APIs offer free or limited access to live datasets. Some key concepts: Authentication: Many APIs require an API key, passed via headers or parameters; Parameters: Customize requests (e.g., by city, category, or date range) using query parameters; Parsing & Storage: API responses (usually JSON) are parsed and stored as DataFrames or exported (e.g., CSV files); Best Practices: Always respect API rate limits, terms of service, and data privacy policies.

Examples to Practice:

import requests
import pandas as pd

# Example: Fetching weather data from OpenWeatherMap API
api_key = 'your_api_key'
url = 'https://api.openweathermap.org/data/2.5/weather'
params = {'q': 'London', 'appid': api_key}

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

if response.status_code == 200:
    data = response.json()
    # Extracting and organizing important fields
    weather_info = {
        'City': data['name'],
        'Temperature': data['main']['temp'],
        'Weather': data['weather'][0]['description']
    }
    df = pd.DataFrame([weather_info])
    print(df)
else:
    print("Failed to retrieve data:", response.status_code)

Explanation of the Example Code: An API key is used for authentication. params dictionary customizes the API call (e.g., specifying the city). JSON response is parsed into a dictionary. Selected fields are extracted and stored in a DataFrame for analysis. Error handling ensures safe, robust requests.

Supplemental Information: Real-world APIs bring your projects to life by connecting Python scripts to live information feeds. However, each API has its own documentation, key policies, and response formats—always review these carefully before integration.

Resources:

Day 4: Combining APIs with Pandas and Visualization

Introduction: Integrating API data with Pandas and visualization libraries allows you to create powerful, end-to-end data analysis workflows—transforming raw external data into meaningful, visual insights.

Learning Objective: By the end of this session, learners will be able to fetch real-world data from APIs, clean and transform it using Pandas, and generate insightful visualizations to communicate findings.

Scope of the Lesson: This lesson covers: Fetching data from an API using requests.get(); Loading and structuring JSON data into Pandas DataFrames; Cleaning and transforming data (e.g., handling missing values, filtering); Creating visualizations, particularly time-series or categorical plots; Building professional data workflows from raw API output to visual reports.

Background Information: APIs provide raw, often unstructured data. Turning this into clean, usable formats involves several steps: Data Fetching: Use requests.get() to pull the data; Data Structuring: Parse JSON responses into DataFrames (pd.DataFrame()); Data Cleaning: Remove missing or irrelevant data (df.dropna(), filtering); Data Transformation: Create new features or recode variables as needed; Data Visualization: Use Seaborn (sns.lineplot, sns.scatterplot) or Matplotlib to explore and present the data clearly. This approach mirrors how professional data scientists work with live data sources to generate reports, dashboards, and predictive models.

Examples to Practice:

import requests
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Example: Fetching and visualizing cryptocurrency prices
url = 'https://api.coindesk.com/v1/bpi/historical/close.json'
response = requests.get(url)

if response.status_code == 200:
    data = response.json()
    # Convert 'bpi' dictionary into DataFrame
    df = pd.DataFrame(list(data['bpi'].items()), columns=['Date', 'Price'])
    df['Date'] = pd.to_datetime(df['Date'])  # Convert Date to datetime type

    # Plotting
    sns.lineplot(x='Date', y='Price', data=df)
    plt.title('Bitcoin Price Over Time')
    plt.xlabel('Date')
    plt.ylabel('Price (USD)')
    plt.xticks(rotation=45)
    plt.show()
else:
    print("Failed to retrieve data:", response.status_code)

Explanation of the Example Code: Data is fetched from the CoinDesk Bitcoin Price Index API. JSON data is parsed and converted into a structured DataFrame. Date values are formatted for correct plotting. A line plot visualizes Bitcoin's historical price trend over time. Clear labels and rotation enhance readability of the final chart.

Supplemental Information: Combining APIs, Pandas, and visualization completes the data pipeline from external sources to insight delivery. Mastery of this skill is critical for real-world analytics roles.

Resources:

Day 5: Capstone API Data Analysis Project

Introduction: This capstone project brings together all the skills learned—API data retrieval, data cleaning and transformation with Pandas, and data visualization—to perform a full analysis on a real-world dataset.

Learning Objective: By the end of this session, learners will complete a mini-project: selecting an API, processing the data, analyzing it, creating visualizations, and delivering a summarized insight report.

Scope of the Lesson: This final project includes: Choosing a public API (e.g., COVID-19 stats, financial data, weather reports); Fetching and parsing JSON data into a Pandas DataFrame; Cleaning the data (handling missing values, duplicates, outliers); Analyzing the dataset (e.g., identifying trends, calculating summary statistics); Visualizing the data with appropriate charts (scatter plots, heatmaps, line plots); Saving visualizations and presenting findings in a brief report.

Background Information: Real-world datasets are often messy and incomplete. Professional data analysis requires: Data Acquisition: Making API calls (possibly authenticated); Data Structuring: Parsing JSON and fitting it into organized DataFrames; Data Cleaning: Removing duplicates (df.drop_duplicates()), handling missing values (df.dropna()), filtering anomalies; Exploratory Analysis: Identifying trends, outliers, or patterns through summary statistics and visual plots; Visualization and Reporting: Producing publication-quality graphs (using Seaborn or Matplotlib) and summarizing insights clearly. This project simulates an end-to-end professional workflow—an essential skill for data analysts and scientists.

Capstone Project Example:

import requests
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Example: Analyzing COVID-19 case data
url = 'https://api.covid19api.com/dayone/country/united-states/status/confirmed/live'
response = requests.get(url)

if response.status_code == 200:
    data = response.json()
    df = pd.DataFrame(data)

    # Data cleaning
    df['Date'] = pd.to_datetime(df['Date'])
    df = df[['Date', 'Cases']]
    df = df.drop_duplicates()

    # Plotting
    plt.figure(figsize=(10, 6))
    sns.lineplot(x='Date', y='Cases', data=df)
    plt.title('COVID-19 Confirmed Cases Over Time in the U.S.')
    plt.xlabel('Date')
    plt.ylabel('Number of Cases')
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.savefig('covid_cases_us.png')
    plt.show()

    # Save a simple summary
    print("Summary Statistics:")
    print(df.describe())
else:
    print("Failed to retrieve data:", response.status_code)

Explanation of the Example Code: COVID-19 data is fetched from a public API. The JSON response is parsed into a Pandas DataFrame. Dates are properly formatted, and duplicate entries are removed. A line plot visualizes case growth over time. A statistical summary provides quick insight into case counts.

Supplemental Information: Executing a full mini-project helps solidify your API, Pandas, and visualization skills, preparing you for larger and more complex data analysis tasks.

Resources:

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