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.

Data Visualization and Analysis With Seaborn Library (1).png

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

**Scatter plots** show the relationship between two variables by plotting every data point into an x-y plane. When we're interested in bivariate analysis, we construct scatter plots to analyze the relationship between two variables.

Let's begin by importing the required libraries `pandas`, `seaborn`, and `matplotlib`. There are two columns in our pandas DataFrame `gym_df` named `time_spent` and `calories_burned`.
 Let's create a scatter plot using the `sns.scatterplot()` function. This function accepts many parameters, but we will only use three here. The first argument is `data`, where we specify our dataset. 
The second argument is `x`, where we specify the variable we want on the x-axis (horizontal line). The third argument is `y`, where we define the y-axis (vertical line) variable. As a standard, we plot independent variables on the x-axis and dependent variables on the y-axis.

import pandas as pd 
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme()             # set default theme
# dummy data
gym_data = {'time_spent': [10,15,20,30,40,50,60,80,90] ,
          'calories_burned': [80,88,100,138,200,300,400,600,710]}  
gym_df = pd.DataFrame(gym_data)  # data frame
sns.scatterplot(data=gym_df , x = 'time_spent' , y = 'calories_burned')
# saving figure
plt.savefig('output/graph.png')

Understanding the correlations between variables differs from simply plotting the variables on a scatter plot. It's crucial to understand what the scatter plot can tell us. The scatter plot may contain one or more of the correlations or patterns discussed below:

- **Linear:** A linear correlation produces a straight line within its data points.

- **Nonlinear:** A nonlinear correlation may have a curve or other shape inside its data points.

- **Strong:** Data points with a strong correlation are closely spaced.

- **Weak:** Data points with a weak correlation are be farther away from each other.

- **Positive:** A positive correlation points upward, meaning both the x- and y-values are rising.

- **Negative:** A negative correlation points downward, meaning the x-values are rising, but the y-values are decreasing.

Learn the fundamentals of scatter plots, including how to design and use them for data visualizations.

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

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

Scatter Plots

Overview

Use, customize, and interpret scatter plots