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Data Visualization using Matplotlib and Seaborn · Lesson 1 of 1

Data Visualization using Matplotlib and Seaborn

Choose charts that match analytical questions and build readable Matplotlib and Seaborn visualizations.

Lesson

1. Introduction

A chart is a visual argument about data. Its job is to make a comparison, trend, distribution, or relationship easier to see without hiding scale, uncertainty, or context.

2. Core concepts

Use position and length for precise comparisons, lines for ordered trends, histograms for distributions, and scatter plots for relationships between numeric values. Seaborn supplies statistical chart defaults built on Matplotlib; both still require thoughtful labels and data choices.

3. Topics covered in this module

  • Figure, axes, labels, legends, scales, and layout
  • Bars, lines, pies, stacked areas, histograms, scatter plots, and subplots
  • Seaborn distributions, categorical plots, themes, and statistical estimates
  • Chart selection, misleading scales, uncertainty, accessibility, and annotation

4. Syntax and how to read it

import matplotlib.pyplot as plt

plt.bar(regions, revenue)
plt.xlabel('Region')
plt.ylabel('Revenue (USD)')
plt.title('Revenue by region')
plt.tight_layout()

Labels and units explain the encoding. Call plt.show() in a script or rely on the notebook display integration to render the figure.

5. Practical workflow

Write the question as a comparison, select a chart that matches the variable types, and show units and sample context. Inspect outliers and missing values before choosing axis limits or smoothing.

6. Common mistakes

  • Using a truncated bar-chart axis that exaggerates small differences.
  • Adding a pie chart with many categories or a chart whose color has no accessible label.

7. Best practices

  • Label axes with units and disclose aggregation and date range.
  • Use color palettes with sufficient contrast and do not rely on color alone.

8. Practice task

You want to show the distribution of delivery times for three regions. Choose a chart and name one useful axis.

9. One possible solution

A box plot or violin plot can compare distributions; use Region on the category axis and Delivery time (days) on the value axis.

10. Summary

Choose charts that match analytical questions and build readable Matplotlib and Seaborn visualizations. Label axes with units and disclose aggregation and date range. Use color palettes with sufficient contrast and do not rely on color alone.

11. What comes next

The next module, Data Collection, Requests, and Web Scraping, builds on this foundation and applies it to a new part of the analytics workflow.

Visual guide

A reliable Data Visualization using Matplotlib and Seaborn workflow

  1. Step 01 Frame

    Define the question, input scope, and intended output.

  2. Step 02 Work

    Apply the method and validate assumptions as you go.

  3. Step 03 Check

    Compare results with known cases and communicate limitations.

Code examples

Plot a time trend
PYTHON 
import matplotlib.pyplot as plt

plt.plot(months, signups, marker='o')
plt.xlabel('Month')
plt.ylabel('Sign-ups')
plt.grid(True, alpha=0.3)
Expected output
A line chart showing sign-ups across ordered months.
Compare distributions by group
PYTHON 
import seaborn as sns

sns.boxplot(data=frame, x='region', y='delivery_days')
Expected output
A distribution comparison of delivery time by region.

Key points

  • Choose charts that match analytical questions and build readable Matplotlib and Seaborn visualizations.
  • Label axes with units and disclose aggregation and date range.
  • Use color palettes with sufficient contrast and do not rely on color alone.

Check your understanding

Practice

Which approach best supports a trustworthy result when working on Data Visualization using Matplotlib and Seaborn?

Your learning progress

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