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1. Exploratory data analysis
2. Example Supermarket sales
   1. Webshop - Assignment
   2. Data
   3. Tasks
      1. Spend pr. group
      2. Linear regression

Exploratory data analysis

In statistics, Exploratory Data Analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods.

A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling and thereby contrasts traditional hypothesis testing.

EDA will give you the basic understanding of your data, it’s distribution, null values and much more. You can either explore data using graphs or through some Python functions.

Example Supermarket sales

We are using a data set from www.kaggle.com with historical record of sales data in 3 different supermarkets.

The dataset consists of data from 3 cities or 3 branches in Myanmar:

• Branch A (Yangoon)
• Branch B (Mandalay)

• Branch C (Naypyitaw)

• Supermarket
• Jupyter Lab file

Webshop - Assignment

Purchases distributed on the different age groups, from a webshop

Data

Data are in a Excel file: spend_age.xlsx

Download - spend_age.xlsx

Tasks

It is your task to make the following:

Spend pr. group

You have to create a “Table” with the mean spend pr. Age group.

Group by:

group_age_spend = df[['Age', 'Spend']].groupby(['Age'], as_index=False).mean().sort_values(by='Age')
group_age_spend

Linear regression