Layering is a data storytelling technique where we reveal our story gradually to help emphasize certain elements of it.

Let's take a look at a brief example of layering with the Walmart store openings dataset.

Consider the following line plot where we draw the number of Walmart stores across the United States from 1962–2006, using the entire dataset:

Python 3.10.4

import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/1962_2006_walmart_store_openings.csv')
#Generate a bar plot using the number of instances of continents
plt.figure(figsize=(10, 7))
fig = df['YEAR'].value_counts().sort_index().plot.line().figure
#Label the axes and title
plt.xlabel("Years")
plt.xticks(rotation=50, horizontalalignment="center")
plt.title("Number of Walmart Stores over the years")
plt.ylabel("Number of Walmart Stores")
fig.savefig('output/to.png')
plt.close(fig)

Introduction

Effective Data Analysis and Visualization

Extracting Narratives from Data

Tackling Real-World Data

Time Series Analysis and Visualization Using Python and Plotly

Data Presentation

Data Types and Sources

Data Visualization with Seaborn for Walmart Sales Projection

Data Storytelling Best Practices

Wrapping Up

Test Your Data Storytelling Skills

Layering

Simple layering case study