Using Pandas and other Python libraries generate and communicate business insights from a grocery sales data set.
A grocery sales company is looking for patterns in its sales data regarding regions, sales reps, and product categories. Conducted an analysis to surface notable patterns related to sales results.
Requirements:
● Import data, including at least two DataFrames and at least one combination of DataFrames via methods such as joining. ● Functions for cleaning the data set, with explanations for how null values are being handled in each field. ● At least two visualizations accompanied by textual descriptions of the business insights they communicate.
Process
Followed this approach for addressing the above problem statement.
- Cleaned the data with rationale-backed handling of null or missing values.
- Joined the data sets together into a single DataFrame.
- Analysed sales results and supply chain logistics in relation to other data points.
After doing data analysis: Proposed a hypothesis around patterns and correlations revealed during the analysis.