Advanced Python for Data Analysis [Week 16]
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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.
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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.
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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:
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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.
- Example: Use the
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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.