Deep Dive into the seaborn Visualization

Deep Dive into the seaborn Visualization

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1. Light Start

In this section, we will perform preparation tasks for the work!

Reading the File

2. Distributions of Data

In this section, we will study plots used for data distributions!

3. Categorical Plot Types

It is time to say 'hello' to categorical plot types!

4. Matrix Plots

Colorful matrix plots are waiting for you!

5. Multi-Plot Grids

These fantastic plots consist of other plots. Let's start this section!

FacetGrid

6. Regression Models

Let's learn plots that are used for regression models!

Course Content

Deep Dive into the seaborn Visualization

Deep Dive into the seaborn Visualization

1. Light Start

Reading the File Colors

2. Distributions of Data

Histplot Kdeplot Rugplot Ecdfplot Challenge Displot

3. Categorical Plot Types

Stripplot Swarmplot Boxplot Violinplot Barplot Pointplot Challenge Catplot

4. Matrix Plots

Heatmap Clustermap

5. Multi-Plot Grids

FacetGrid PairGrid JointGrid

6. Regression Models

FacetGrid

The time to learn the cool plots we were talking about!

A useful approach to exploring medium-dimensional data is by drawing multiple instances of the same plot on different subsets of your dataset.

FacetGrid object takes a DataFrame as input and the names of the variables that will form the grid's row, column, or hue dimensions. The variables should be categorical, and the data at each level of the variable will be used for a facet along that axis.

Task

Swipe to show code editor

Set the 'whitegrid' style with the 'cornsilk' axes.facecolor.
Create a FacetGrid variable g:

Set the data for the FacetGrid;
Set the col parameter equals the 'day';
Set the row parameter equals the 'smoker';
Set the height parameter equals 3.

Implement the .map() function to build histplots:

Create histplots;
Set the 'olive' color for the histplots;
Add the kde parameter;
Disable the fill parameter;
Set the binwidth equals 4;
Display the plot.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 5. Chapter 1

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FacetGrid

The time to learn the cool plots we were talking about!

A useful approach to exploring medium-dimensional data is by drawing multiple instances of the same plot on different subsets of your dataset.

FacetGrid object takes a DataFrame as input and the names of the variables that will form the grid's row, column, or hue dimensions. The variables should be categorical, and the data at each level of the variable will be used for a facet along that axis.

Task

Swipe to show code editor

Set the 'whitegrid' style with the 'cornsilk' axes.facecolor.
Create a FacetGrid variable g:

Set the data for the FacetGrid;
Set the col parameter equals the 'day';
Set the row parameter equals the 'smoker';
Set the height parameter equals 3.

Implement the .map() function to build histplots:

Create histplots;
Set the 'olive' color for the histplots;
Add the kde parameter;
Disable the fill parameter;
Set the binwidth equals 4;
Display the plot.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 5. Chapter 1

toggle bottom row

FacetGrid

The time to learn the cool plots we were talking about!

A useful approach to exploring medium-dimensional data is by drawing multiple instances of the same plot on different subsets of your dataset.

FacetGrid object takes a DataFrame as input and the names of the variables that will form the grid's row, column, or hue dimensions. The variables should be categorical, and the data at each level of the variable will be used for a facet along that axis.

Task

Swipe to show code editor

Set the 'whitegrid' style with the 'cornsilk' axes.facecolor.
Create a FacetGrid variable g:

Set the data for the FacetGrid;
Set the col parameter equals the 'day';
Set the row parameter equals the 'smoker';
Set the height parameter equals 3.

Implement the .map() function to build histplots:

Create histplots;
Set the 'olive' color for the histplots;
Add the kde parameter;
Disable the fill parameter;
Set the binwidth equals 4;
Display the plot.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

The time to learn the cool plots we were talking about!

A useful approach to exploring medium-dimensional data is by drawing multiple instances of the same plot on different subsets of your dataset.

FacetGrid object takes a DataFrame as input and the names of the variables that will form the grid's row, column, or hue dimensions. The variables should be categorical, and the data at each level of the variable will be used for a facet along that axis.

Task

Swipe to show code editor

Set the 'whitegrid' style with the 'cornsilk' axes.facecolor.
Create a FacetGrid variable g:

Set the data for the FacetGrid;
Set the col parameter equals the 'day';
Set the row parameter equals the 'smoker';
Set the height parameter equals 3.

Implement the .map() function to build histplots:

Create histplots;
Set the 'olive' color for the histplots;
Add the kde parameter;
Disable the fill parameter;
Set the binwidth equals 4;
Display the plot.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 5. Chapter 1

Switch to desktop for real-world practiceContinue from where you are using one of the options below

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