Delve into data visualization and analysis with Python's Seaborn library. Learn about variable types, data cleaning, creating detailed visualizations, and applying themes for more appealing results.

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jupyter.tar.gz

Adding Hue Parameter for color encoding

Data cleaning with Pandas

Getting Started with Seaborn

Intro to Pandas

Line Plots in Seaborn

Pair Plot

Scatter Plot

libraries

project

solution

This course aims to provide an introduction to data visualization and analysis using Python and the Seaborn library.

The course begins by introducing various variable types and statistical analysis methods. Then, you get to review the foundations of data cleaning and extraction using the pandas library. In the second half of the course, you will go over different plots in Seaborn for numerical, continuous, and categorical data, as well as distribution and regression plots to gain insightful information and identify patterns in the data. Lastly, you get to learn to create complex visualizations that are also aesthetically pleasing and go into great detail about the Seaborn themes, color palettes, styling, and multiplot grids.

By the end of this course, you’ll apply the knowledge you’ve gained with a hands-on project.

Data Visualization and Analysis With Seaborn Library

**pandas** is an open-source Python library that provides efficient data manipulation and analysis tools. It offers a variety of data structures and procedures along with support for various data formats. It's built on top of Python's NumPy library. 

The following key features are why pandas is a popular and commonly used library:

- Fast, efficient, and optimal performance and support for big data.
- Support for various data formats such as CSV files, JSON, XML, and SQL databases (to name a few).
- Data cleaning and support for handling missing values.

Python's `pandas` library provides support for the following two data structures:

- pandas Series
- pandas DataFrame

The pandas **Series** object is a one-dimensional labeled array. We can populate a Series object with any Python data type, such as integers, strings, floats, and so on. We can think of the Series object as a column in a spreadsheet. All Series objects are indexed by default, meaning that every Series element has an index. 

We can create a Series object using an array, dictionary, lists, and scalars. As illustrated in the figure below, we can make the `scores` Series object by passing the `student_score` list to the `pd.Series()` function. Each element is indexed, and we can access any element using its index. Here, we use the default alias `pd` to refer to the `pandas` library. 

Learn the fundamentals of Python's pandas library.

About the Course

Introduction to Seaborn and Statistical Analysis

Plotting Numerical Data

Assessment on Plotting Numerical Data

Plotting Categorical Data

Plotting the Categorical Data

Assessment on Categorical Plots

Visualizing Distribution of Data

Data Visualization with Seaborn for Walmart Sales Projection

Assessment on Distributions of Data

Visualizing Regression Models

Assessment on Visualization of Regression Models

Styling and Figure Aesthetics

Assessment on Styling and Figure Aesthetics

Multiplot Grids

Assessment on Multi-Plot Grids

Project

Closing Remarks

Introduction to pandas

Why pandas?

Pandas data structures

The pandas series