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

Contenido del Curso

Analyzing and Visualizing Real-World Data

Analyzing and Visualizing Real-World Data

What Holidays are we Considering?

Interesting! We can see that the median revenue for weeks with holidays is greater than for weeks without holidays. Let's find out which holidays are included in the dataset and summarize this information in a table.


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# Loading the library
import pandas as pd

# Reading the data
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data3.csv')
df['Date'] = pd.to_datetime(df['Date'], dayfirst = True)

# Filtering to only weeks with holiday
print(df.loc[df['Holiday_Flag'] == 1]['Date'].unique())

So, what holidays do we have? Let's summarize this information in the table.

Dates (in the dataframe)	Holiday
2010-02-12, 2011-02-11, 2012-02-10	Super Bowl
2010-09-10, 2011-09-09, 2012-09-07	Labor Day
2010-11-26, 2011-11-25	Thanksgiving
2010-12-31, 2011-12-30	Christmas

Additionally, according to the result obtained in the previous chapter, we should include one week before Christmas, too.

Dates (in the dataframe)	Holiday
2010-12-24, 2011-12-23	'Pre-Christmas'

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 3. Capítulo 8

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