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

Course Content

Analyzing and Visualizing Real-World Data

Analyzing and Visualizing Real-World Data

The Most Profitable Store

Since we want to find the most profitable stores based on the given data, we need to group the observations in the df dataframe by the 'Store' column values and calculate the total (sum) of the 'Weekly_Sales' column for each group. Finally, we need to sort the resulting values in descending order.

Task

Swipe to show code editor

Group the observations in the df dataframe by the 'Store' column values.
Select the 'Weekly_Sales' column.
Calculate the total (sum) for each group.
Sort the resulting values in descending order (not ascending).

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Section 3. Chapter 2

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The Most Profitable Store

Since we want to find the most profitable stores based on the given data, we need to group the observations in the df dataframe by the 'Store' column values and calculate the total (sum) of the 'Weekly_Sales' column for each group. Finally, we need to sort the resulting values in descending order.

Task

Swipe to show code editor

Group the observations in the df dataframe by the 'Store' column values.
Select the 'Weekly_Sales' column.
Calculate the total (sum) for each group.
Sort the resulting values in descending order (not ascending).

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 2

toggle bottom row

The Most Profitable Store

Since we want to find the most profitable stores based on the given data, we need to group the observations in the df dataframe by the 'Store' column values and calculate the total (sum) of the 'Weekly_Sales' column for each group. Finally, we need to sort the resulting values in descending order.

Task

Swipe to show code editor

Group the observations in the df dataframe by the 'Store' column values.
Select the 'Weekly_Sales' column.
Calculate the total (sum) for each group.
Sort the resulting values in descending order (not ascending).

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Since we want to find the most profitable stores based on the given data, we need to group the observations in the df dataframe by the 'Store' column values and calculate the total (sum) of the 'Weekly_Sales' column for each group. Finally, we need to sort the resulting values in descending order.

Task

Swipe to show code editor

Group the observations in the df dataframe by the 'Store' column values.
Select the 'Weekly_Sales' column.
Calculate the total (sum) for each group.
Sort the resulting values in descending order (not ascending).

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 3. Chapter 2

Switch to desktop for real-world practiceContinue from where you are using one of the options below

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