Analyzing and Visualizing Real-World Data

Analyzing and Visualizing Real-World Data

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1. Preprocessing Data: Part I

First, let's get acquainted with the data we will be working with. Throughout this section, we will perform the initial set of actions to prepare the data for further analysis.

Loading the Data

Columns Overview

Removing Characters: Method 1

Removing Characters: Method 2

Checking Columns

Replacing Characters

2. Preprocessing Data: Part II

In the second section, we will continue with the preparatory tasks started in the first section and ensure the data is ready for further exploration.

Analyzing Columns

Removing and Replacing

Processing Dates

Wrong Convertation?

Proper Converting

3. Analyzing Data

After preparing the data, we will move on to some exploratory analysis. Throughout this section, we will answer several questions and uncover interesting insights.

Superficial Analysis

The Most Profitable Store

How Periodic the Data is?

The Most Profitable Periods

The Best Combination

Holidays Effect

What Holidays are we Considering?

The Most Profitable Holiday

4. Visualizing Data

In the last section, we will use visualization tools to either confirm or reject the hypotheses we made in the previous section. We will also create visualizations of key indicators for the entire dataset.

First Look at the Data

Comparing Dynamics

Finding Relation

Does Temperature Affect?

Or is it Holiday Effect?

Comparing Shops

Adding Holidays to Comparison

Holidays Effect

The result of the previous chapter is quite interesting! It turns out that the top 10 combinations of store and week with the highest sales are during the last but one week of December! What could be the reason for this? Perhaps the upcoming Christmas - people start preparing for the holiday, buying gifts, and so on.

There is a column called 'Holiday_Flag' with only 2 possible values: 1 if the week contains a major holiday, and 0 if it does not. Let's compare the median sales for weeks with and without holidays.

Task

Group the values of the df dataframe by the 'Holiday_Flag' column.
Select the 'Weekly_Sales' column.
Calculate the median values across the groups.

Everything was clear?

Section 3. Chapter 7

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Course Content

Analyzing and Visualizing Real-World Data

Analyzing and Visualizing Real-World Data

Holidays Effect

The result of the previous chapter is quite interesting! It turns out that the top 10 combinations of store and week with the highest sales are during the last but one week of December! What could be the reason for this? Perhaps the upcoming Christmas - people start preparing for the holiday, buying gifts, and so on.

There is a column called 'Holiday_Flag' with only 2 possible values: 1 if the week contains a major holiday, and 0 if it does not. Let's compare the median sales for weeks with and without holidays.

Task

Group the values of the df dataframe by the 'Holiday_Flag' column.
Select the 'Weekly_Sales' column.
Calculate the median values across the groups.

Everything was clear?

Section 3. Chapter 7

toggle bottom row

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