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

Processing Dates

We are close to finishing! Let's see what data types our data has now.

As you can see, there is only one object column left. You may be wondering why this is bad. Actually, we can leave it as is, but pandas provides us with tools to work with dates and times. Unfortunately, datetime is not a built-in data type in Python, so we can't convert the column and save it. We will have to convert it after loading every time. To convert to datetime type, we need to use the .to_datetime() method of pd.

As you can see, visually the dates have changed, i.e., they are now represented in a different format.

Everything was clear?

Section 2. Chapter 3

Course Content

Analyzing and Visualizing Real-World Data

Analyzing and Visualizing Real-World Data

Processing Dates

We are close to finishing! Let's see what data types our data has now.

As you can see, there is only one object column left. You may be wondering why this is bad. Actually, we can leave it as is, but pandas provides us with tools to work with dates and times. Unfortunately, datetime is not a built-in data type in Python, so we can't convert the column and save it. We will have to convert it after loading every time. To convert to datetime type, we need to use the .to_datetime() method of pd.

As you can see, visually the dates have changed, i.e., they are now represented in a different format.

Everything was clear?

Section 2. Chapter 3

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