EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas
This article teaches how to analyze product performance, extract time-series features, and uncover key seasonal trends in sales data using Pandas.
Why it matters
Understanding product performance and seasonal trends is crucial for making informed business decisions and optimizing sales strategies.
Key Points
- 1Analyze product performance
- 2Extract time-series features
- 3Uncover seasonal trends in sales data
Details
This article is part of a series on Exploratory Data Analysis (EDA) in public. It focuses on techniques for analyzing product performance, extracting time-series features, and uncovering seasonal trends in sales data using the Pandas library. The article provides a step-by-step guide on how to perform these analyses, including visualizations and code examples. The goal is to help readers gain insights into their product data and identify key patterns that can inform business decisions.
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