Course Content

Ultimate Visualization with Python

1. Matplotlib Introduction

Plotting Applications Why Use Matplotlib?Creating a Canvas Basic Plotting

2. Creating Commonly Used Plots

Line Plot Multiple Line Plots Scatter Plot Bar Chart Stacked Bar Charts Grouped Bar Charts

3. Plots Customization

Adding Title to the Plot Adding Legend Axes Customization Colors and Transparency Customizing Grid Subplots

4. More Statistical Plots

Histogram Box Plot Pie Chart

5. Plotting with Seaborn

Seaborn Introduction Countplot Plots Decoration KDE Plot Joint Plot Pair Plot Heatmap

Ultimate Visualization with Python

Plotting Applications

After finishing this course you will be able to deal with the following tasks in our workspaces block. To work with the workspace you should follow the link and copy the workspace:

Simple Sales Analysis

In a quickly developing world we are living in, the amount of data is constantly growing. Thankfully, plotting can help us make sense of the data we are interested in via transforming raw data into meaningful visualizations.

Main applications of plotting are the following:

Communication (plotting helps convey complex ideas and information to a wide range of audiences)
Analytics (helpful providing a clear overview of data distribution, relationships, and correlations)
Decision making (instrumental in creating reports and dashboards that assist in strategic decision-making)

Suppose you run a business, and you would like to look at the sales of your products from January to June. Here is a plot to make everything clear for you:

This is just an example for you with some artificial data, yet you can see not only the number of sales by month, but certain trends for each of the products.

For instance, watch sales have clearly decreased in March and June, which means that these months in particular should be analyzed to explain this significant drop in regard to watch sales.

Everything was clear?

Section 1. Chapter 1