Python [Week 11-16]

Advanced Python for Data Analysis [Week 16]

  • Pivot Tables: Create multi-dimensional data summaries similar to Excel pivot tables.

    • Example: Analyze sales data by region, product category, and time period simultaneously using Pandas pivot tables.
  • Time Series Analysis: Manipulate and analyze time-series data, including indexing by dates and resampling data.

    • Example: Analyze stock prices or sales data over time to find seasonal trends.
  • MultiIndexing: Work with hierarchical indexes in Pandas to perform operations across multiple levels.

    • Example: Sales data for multiple regions and products can be indexed by both, allowing for more granular analysis.

B. Working with APIs and Web Scraping:

  • APIs: Retrieve data from online sources (e.g., social media data, financial data) using APIs.

    • Example: Use the requests library to pull data from a weather API and analyze trends over time.
  • Web Scraping: Collect data from websites using tools like BeautifulSoup and Scrapy.

    • Example: Scrape product prices from an e-commerce website to compare pricing strategies across competitors.

Why Advanced Python is Essential for Data Analysis:
As you handle more complex datasets, you’ll need to work with API data, manage large datasets, and perform more sophisticated data wrangling tasks. Advanced Python concepts, including time-series analysis and web scraping, will help you automate data collection and extend your analytical capabilities.