1. Introduction

In this tutorial, our primary goal is to provide an introduction to data visualization in Python. We will guide you on how to extract valuable insights from your data and present it visually using Python libraries.

By the end of this tutorial, you will learn how to:
- Understand the significance of data visualization
- Use different Python libraries for data visualization
- Create various types of plots and charts

Before getting started, you should have a basic understanding of Python programming. Familiarity with Pandas and NumPy would be beneficial but not strictly necessary.

2. Step-by-Step Guide

Data visualization is the graphical representation of information and data. It uses visual elements like charts, graphs, and maps to see and understand trends, outliers, and patterns in data.

Python provides several libraries for data visualization like Matplotlib, Seaborn, Plotly etc.

Matplotlib

Matplotlib is a plotting library for Python. It is used along with NumPy to provide an environment that is an effective open source alternative for MatLab.

Seaborn

Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Plotly

Plotly is a Python graphing library makes interactive, publication-quality graphs online.

3. Code Examples

Example 1:

Let's start with a simple line graph using Matplotlib.

# Importing matplotlib
import matplotlib.pyplot as plt

# Create a simple list of categories
x = ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries']

# Create a simple list of values
y = [5, 7, 6, 3, 7]

# Create a bar chart
plt.bar(x, y)

# Show the plot
plt.show()

In this code, we first import the matplotlib.pyplot module. We then define two lists, 'x' and 'y'. The 'plt.bar()' function creates the bar chart, and 'plt.show()' displays the plot.

Example 2:

Let's create a scatterplot using seaborn.

# Import seaborn
import seaborn as sns

# Load the example iris dataset
iris = sns.load_dataset("iris")

# Create a scatterplot from the dataframe
sns.scatterplot(x="sepal_length", y="sepal_width", data=iris)

# Show the plot
plt.show()

We first import seaborn and load the iris dataset. The 'sns.scatterplot()' function is used to create a scatterplot, and 'plt.show()' displays it.

4. Summary

In this tutorial, we've covered the basics of data visualization in Python, including how to use libraries like Matplotlib and Seaborn. You've learned how to create bar charts and scatter plots to visually represent your data.

To continue your learning, you could explore other types of plots like histograms, box plots, and more complex visualizations like heatmaps. You could also learn more about how to customize your plots with titles, labels, and colors.

5. Practice Exercises

Here are some exercises for you to practice:

1. Create a histogram using any dataset. You can use the iris dataset, which is available in seaborn.
2. Create a box plot representing five different categories of your choice.
3. Create a heatmap of a correlation matrix for any dataset.

Remember, the more you practice, the better you'll get. Happy coding!