Data Wrangling Assignment ~ The Origin for Screamers

Data Wrangling Assignment

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Data Wrangling Assignment

Table of Contents:

1. Introduction

2. Data Gathering

3. Data Merging

4. Data Analysis and Results

1. Introduction :

Local Ice-Cream Store is an Ice-cream multistore at which ice-cream sales are made across different regions, stores and sales of the different ice-creams. This report helps to understand the analysis made on the data and provides insightful information. This helps to understand more on the sales of ice creams across different regions.

2. Data Gathering :

There are different data sources shared to perform the analysis to identify ice-cream sales trend.

· Ice-Cream Sales data given at weekly level - Weekly_Sales.csv

· Regions data file given for regions wise analysis – Regions.csv

· Stores data for understand on the temperature and unemployment rate – StoreDetails.csv

3. Data Merging :

· Sales data is initially merged with the stores data using the key “Store”.

· Merged Sales data and the stores data is merged with regions file to generate a master merged file.

4. Data Analysis:

Graph -1: Mean Sales Across years

Graph-1: Analysis and Results

· Weekly Sales if aggregated to derive average sales across years 2010, 2011 and 2012.

· The above graph shows Year 2010 has highest sales compared to that of sales in 2011 and 2012.

· This trend indicates, Average Sales decreasing from 2010 to 2012.

Graph -2: Average weekly Sales Across stores.

Graph – 2: Analysis and Results

· Weekly Sales is aggregated to derive average sales across all the stores.

· Above graphs, helps to understand the sales across different stores and identify the poor performance stores in terms of revenue generation.

· It clearly states that Store 10, 13 and 20 are generating high revenue as the sales are high.

· Sales across stores 3, 5, 33, 36,38,42,43 and 44 are very low and revenue generation would not be high.

Graph -3: Average Store temperatures across all the stores.

Graph -3: Analysis and Results

· Average store temperatures, Temperatures are aggregated to calculate average store temperatures across all the stores.

· Analyzing the above graph, there is no insightful information found as most of the stores have equivalent average store temperatures.

Graph - 4: Average Employment rate across all the stores.

Graph - 4: Analysis and Results

· Average store employment rate across all the stores, given employment rate data across all the stores are aggregated to calculate average store employment rate across all the stores.

· Above Graph clearly shows Stores 12, 28 and 38 are with high employment rate.