Seaborn Mastery: Comprehensive Data Visualization in Python

Name: Seaborn Mastery: Comprehensive Data Visualization in Python
Rating: 4.4 (45 reviews)

Unlock full potential of data visualization with Seaborn in Python for insightful analysis and compelling presentations

Created byEDUCBA Bridging the Gap

Last updated 3/2024

English

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What you'll learn

Introduction to Seaborn: Understand the basics of Seaborn, a powerful Python data visualization library.
Scatter Plot and Line Plots: Learn how to create scatter plots and line plots to visualize relationships between variables.
Categorical Scatterplots and Distributions: Explore different types of categorical scatterplots and distributions of observations within categories.
Statistical Estimation and Countplot Examples: Dive into statistical estimation within categories and examples of countplot.
Advanced Techniques: Discover intermediate and advanced techniques such as conditioning on other variables and fitting different kinds of models.
Custom Functions and Pairwise Data Relationships: Use custom functions and plot pairwise data relationships with Seaborn.
Case Study: Apply Seaborn to a real-world case study using Census dataset, performing exploratory data analysis and creating various visualizations.

Course content

4 sections • 51 lectures • 6h 49m total length

Introduction of Seaborn1:28
Explore Seaborn basics by creating scatter, line, count, point plots, box, violin, bar, swarm, and strip plots with Seaborn and Matplotlib.
Scatter Plot Part 111:32
Explore visualizing statistical relationships with seaborn scatter plots that reveal the joint distribution of two variables, and use color, size, and style to encode additional variables and group differences.
Scatter Plot Part 27:18
Master seaborn scatter plots by using different continuous color maps like the cube helix palette and mapping point size to data, with legends.
Line Plots Part 19:31
Master seaborn line plots for time-based data, control sorting and confidence intervals, and use semantic mappings to color, style, and markers for comparing multiple data subsets.
Line Plots Part 28:36
Learn line plots in Seaborn for repeated measures data by plotting each sampling unit separately and customizing color maps, palettes, and legends for semantic versus numeric variables.
Showing Multiple Relationships with Facets8:38
Learn to visualize multiple relationships with seaborn facets and facet grids, exploring how an additional variable influences relationships across subplots for easy interpretation.
Categorical Scatterplots9:34
Learn to visualize categorical data with seaborn using a unified figure level api, exploring scatter and strip plots, swarm, box, bar, and point plots to compare categories and distributions.
Distributions of Observations within Categories8:50
Explore how seaborn summarizes distributions within categories using box plots that show quartiles, whiskers, and outliers, with dogging to prevent overlap and options like boiling plots with kernel density estimates.
Statistical Estimation within Categories10:06
Explore statistical estimations within categories using Seaborn bar plots, count plots, and point plots, including mean estimates, bootstrapped confidence intervals, and facet grids for complex relationships.
Countplot Examples7:12
Explore countplot examples in Seaborn using the Titanic dataset, demonstrating x and y configurations, color palettes, legends, and horizontal and vertical orientations.
Pointplot Examples8:43
Explore seaborn pointplot examples, creating vertical and horizontal dot plots with two-variable grouping, color palettes, and central-tendency options like median and standard error.
Boxenplot Examples11:22
Learn to create boxenplots and violin plots in Seaborn, including horizontal and vertical orientations, nested and explicit category orders, and enhancements with strip plots and catplot for grouped comparisons.
Violenplot Examples6:18
Explore violin plot examples in seaborn, adjusting scale, density relative to counts, inner lines, bandwidth, and split options, and compare horizontal and vertical violins using planets orbital period filters.
Barplot Examples8:03
Explore seaborn bar plots, including vertical and horizontal bars, grouped and multi-variable designs, with custom order, error bars, median-based central tendency, and color palettes.
Swarmplot Examples6:46
Explore Seaborn swarmplot examples in Python, including combining swarm plots with box plots, coloring by category, setting explicit orders, scaling points, and introducing strip plots.
Stripplot Examples8:24
Learn to create and customize strip plots in seaborn, including jitter, horizontal orientation, grouping by category, and overlaying with box plots for detailed distribution insights.
Catplot Examples7:55
Explore how to create and customize catplots in Seaborn, including single and multi-facet visualizations, legend placement, and axis labeling.

Introduction to Seaborn Intermediate1:22
Move from Seaborn introductory basics to intermediate plotting, learning to visualize univariate and bivariate distributions with kde plot, dist plot, pair plot, joint plot, and lm plot through hands-on demonstrations.
Plotting Univariate Distributions11:12
Explore plotting univariate distributions with seaborn in Python, using distplot to visualize histograms and KDE, add rug plots, adjust bins and bandwidth, and fit parametric distributions like gamma.
Plotting Bivariate Distributions9:43
Explore plotting bivariate distributions in Seaborn with joint plot, scatter plots, x-bin hex bins, and kernel density estimation; compare joint grid and pair plot for multiple relationships.
Functions to Draw linear Regression Models7:31
Visualize linear relationships with seaborn regression plots, using regplot and lmplot to fit a regression line with a 95% confidence interval from scatter data, while emphasizing exploratory data analysis.
Fitting Different Kinds of Models7:29
Explore fitting models in Seaborn, including polynomial regression with lm plot, robust and logistic regression (for outliers and binary data), and loess smoothing, with residual plots and bootstrap confidence intervals.
Conditioning on Other Variables8:53
Explore how the relationship between two variables changes with a third variable using Seaborn, including lm plots, facet grid, color and markers, and joint and pair plots for tips dataset.
Examples on KDEPLOT10:51
Explore Seaborn visualizations with dot plots and density plots, including univariate and bivariate densities, contour shading, color palettes, and dist plots with kernel density estimates, histograms, and fits.
Examples on PAIRPLOT6:30
learn to create seaborn pair plots and joint plots with iris data, exploring scatter plots, histograms, hue by species, and multiple palettes and markers.
JOINTPLOT and LMPLOT9:24
Master seaborn jointplot and lmplot techniques in Python, exploring regression, kernel density estimates, hex bins, and configurable marginals using iris and tips datasets.

Introduction to Seaborn Advance1:27
Advance your Seaborn mastery with hands-on demonstrations of multi-plot grids, figure styles, and color palettes, following basic and intermediate prerequisites and noting Matplotlib is required.
Conditional Small Multiples8:44
Create structured multi-plot grids with Seaborn facet grid to enable lattice or trellis plotting and small multiples across tidy pandas data.
Conditional Small Multiples Continue11:40
Learn to control seaborn facet grid ordering using category types or order parameter, color plots with palettes or hue dictionaries, and map dist plots and scatter plots with axis labeling.
Using Custom Functions8:55
Define and map custom functions in seaborn facetgrid, ensuring correct axes, support for color and label kwargs, and demonstrate a qq plot and hex bin with total_bill and tip.
Plotting Pairwise Data Relationships10:15
visualize pairwise data relationships with seaborn using pair grid or pair plot, showing each variable pair, diagonal distributions, and optional hue by species with customizable upper, lower, and diagonal plots.
Extra Examples Part 111:09
Explore advanced Seaborn facet grid and pair grid techniques, visualizing histograms, scatter plots, a polar projection, and densities across facets using the tips dataset.
Extra Examples Part 212:01
Using Diff Seaborn Figure Styles12:56
Setting Different Color Palettes10:45
Explore how to set and customize color palettes in seaborn, including qualitative palettes, lightness and saturation controls, and practical examples with color palette sets, xkcd, and cube helix.
Setting Different Color Palettes Continue6:52
Explore how to apply and customize seaborn color palettes, including light, dark, and diverging palettes, create and reverse palettes, and use cmap with KDE plots and matplotlib.

Introduction of Project2:25
Build intermediate data visualizations with Seaborn in Python, using census data to perform EDA, install Seaborn and Matplotlib, create attractive graphs, and generate analysis reports.
Installation of Tools7:26
Install matplotlib and seaborn, set up Python with Anaconda, and run projects in Jupyter or Spyder, using pip or conda to manage dependencies like NumPy, SciPy, pandas, and matplotlib.
Libraries7:30
Learn how to install seaborn using conda navigator, pip, or anaconda prompt, verify with import seaborn, and understand required dependencies like NumPy, SciPy, pandas, and matplotlib.
Exploratory Data Analysis5:38
Open the exploratory data analysis by importing numpy, pandas, seaborn (sns), and matplotlib, loading census data with read_csv, and inspecting the dataframe with head while naming columns.
Add Columns to Dataset6:09
Learn how to add and rename dataset column names, inspect data shape and types, and run descriptive statistics, then check for null values before visualizing with seaborn and matplotlib.
Data Visualization Scatterplot10:54
Visualize relationships between numerical variables with seaborn scatter plots, such as capital loss vs capital gain. Use hue, column, and size to explore effects by marital status and gender.
Multiple Line Plot4:19
Explore line plots with sns to show age versus hours per week, add error bars, and build multi-line comparisons by gender.
Swarm plot10:08
Explore seaborn catplot to visualize relationships between age and education across categories, using swarm and jitter plots, and learn to rotate x-axis labels to reduce overlap.
X and Y Tick Label Setting9:22
Seaborn tick label settings and plot customization, from rotating x and y labels to font size, weight, and color, while exploring violin, bar, and cat plots for distribution analysis.
Violin Plot and Bar plot9:03
Create count plots and bar plots with Seaborn catplot, adjust x labels, and compare marital status categories. Explore univariate analysis with distribution plots and histograms in Seaborn.
Point plot5:51
Explore how marginal plots reveal bivariate and univariate relationships using Seaborn's jointplot, demonstrating scatter, hex, and kde visuals with histograms at the margins.
Heat map6:04
Master seaborn heat maps to explore correlations across dataset columns, using univariate and bivariate views, marginal plots, color options, space tuning, and optional regression lines to reveal relationships.
Pair plot5:34
Create heat maps to visualize correlations among numerical columns with seaborn, adjusting vmax, color, and square layout; explore box plots and pair plots to reveal distributions and relationships.
lmplot6:59
Use seaborn lmplot to produce a 2d scatter plot with a regression line, comparing capital gain and capital loss by gender; facet by column or row and adjust markers.
Pair grid4:32
Learn to create a pair grid in seaborn, drawing scatter plots and diagonal histograms, and customize the upper and lower triangles with map methods.

Requirements

The basic prerequisite for this course is that the student or the professional should have a basic knowledge and understanding of the machine learning tools and techniques and also should have a basic knowledge and overview of the data science techniques. Apart from this, he should also be aware of the basic analytical concepts which are a must while opting for this course. The user should also have a mathematical background as most of the algorithms being used and the concepts which are discussed are mathematics-based.

Description

Welcome to the "Seaborn Python Mastery: From Beginner to Advanced" course! Seaborn is a powerful Python library for creating informative and visually appealing statistical graphics. Whether you're a beginner or an experienced data scientist, this course will take you on a comprehensive journey through Seaborn, starting from the basics and gradually progressing to advanced topics.

Throughout this course, you will learn how to leverage Seaborn to visualize data distributions, relationships, and patterns effectively. From simple scatter plots to complex conditional small multiples, you will master a wide range of visualization techniques to extract meaningful insights from your data.

With hands-on exercises and real-world case studies, you'll gain practical experience in applying Seaborn to real-world data analysis tasks. By the end of the course, you'll be equipped with the skills and knowledge to create stunning visualizations that communicate your data insights effectively.

Join us on this exciting journey and unlock the full potential of Seaborn for your data visualization needs!

Section 1: Seaborn Python - Beginners

In this introductory section, students will familiarize themselves with Seaborn, a Python library built on top of Matplotlib that facilitates the creation of informative and visually appealing statistical graphics. They will start by understanding the fundamental concepts of Seaborn and its advantages over other visualization libraries. The lectures will cover essential plot types such as scatter plots, line plots, and categorical scatterplots. Students will learn how to create these plots using Seaborn and gain insights into their interpretation and usage in data analysis tasks. Additionally, they will explore more advanced visualization techniques like box plots, violin plots, and bar plots, enabling them to effectively represent data distributions and relationships.

Section 2: Seaborn Python - Intermediate

Building upon the foundational knowledge from the beginner section, students will delve deeper into Seaborn's capabilities in the intermediate section. They will learn how to visualize univariate and bivariate distributions using functions like DISTPLOT and JOINTPLOT. Additionally, students will explore the use of regression plots to understand the relationships between variables and how to customize them using different parameters. The lectures will also cover advanced topics such as conditional small multiples, where students will learn to create multiple plots based on different conditions, providing deeper insights into the data.

Section 3: Seaborn Python - Advanced

In the advanced section, students will further enhance their proficiency in Seaborn by mastering more complex visualization techniques. They will learn how to use custom functions to create specialized plots and effectively visualize pairwise relationships between variables. The lectures will also cover advanced styling options such as setting different color palettes and themes to enhance the aesthetic appeal of the visualizations. Additionally, students will explore the use of PairGrid to create a grid of subplots for visualizing multiple pairwise relationships simultaneously.

Section 4: Seaborn Python Case Study - Data Visualization using Seaborn on Census Dataset

In this practical section, students will apply their knowledge of Seaborn to a real-world case study involving the visualization of census data. They will gain hands-on experience in performing exploratory data analysis (EDA) to gain insights into the dataset's structure and characteristics. Students will learn how to preprocess the data, add new columns, and perform various visualizations using Seaborn. By the end of this section, students will have the skills to effectively visualize complex datasets and communicate their findings through compelling visualizations.

Who this course is for:

Python enthusiasts looking to enhance their data visualization skills using Seaborn.
Data analysts and scientists seeking to leverage Seaborn for exploring and presenting data insights effectively.
Students and professionals in fields like data science, statistics, and research who want to add Seaborn proficiency to their toolkit.
Anyone interested in learning advanced data visualization techniques in Python for insightful analysis and presentation of data.

Seaborn Mastery: Comprehensive Data Visualization in Python

What you'll learn

Explore related topics

Course content

Seaborn Python - Beginners17 lectures • 2hr 20min

Seaborn Python - Intermediate9 lectures • 1hr 13min

Seaborn Python - Advanced10 lectures • 1hr 35min

Seaborn Python Case Study - Data Visualization using Seaborn on Census Dataset15 lectures • 1hr 42min

Requirements

Description

Who this course is for: