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!

6. Regression Models

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

Зміст курсу

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

Ecdfplot

An ecdfplot represents the proportion or count of observations falling below each unique value in a dataset. Compared to a histogram or density plot, it has the advantage that each observation is visualized directly, meaning that no binning or smoothing parameters need to be adjusted.

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Завдання

Create the ecdfplot using the seaborn library:

Set the x parameter equals the bill_length_mm;
Set the hue parameter equals the 'island';
Add the complementary parameter;
Set the stat parameter equals the 'count';
Set the palette equals the 'mako';
Set the data.

Завдання

Create the ecdfplot using the seaborn library:

Set the x parameter equals the bill_length_mm;
Set the hue parameter equals the 'island';
Add the complementary parameter;
Set the stat parameter equals the 'count';
Set the palette equals the 'mako';
Set the data.

Все було зрозуміло?

Секція 2. Розділ 4

toggle bottom row

Ecdfplot

An ecdfplot represents the proportion or count of observations falling below each unique value in a dataset. Compared to a histogram or density plot, it has the advantage that each observation is visualized directly, meaning that no binning or smoothing parameters need to be adjusted.

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

Завдання

Create the ecdfplot using the seaborn library:

Set the x parameter equals the bill_length_mm;
Set the hue parameter equals the 'island';
Add the complementary parameter;
Set the stat parameter equals the 'count';
Set the palette equals the 'mako';
Set the data.

Завдання

Create the ecdfplot using the seaborn library:

Set the x parameter equals the bill_length_mm;
Set the hue parameter equals the 'island';
Add the complementary parameter;
Set the stat parameter equals the 'count';
Set the palette equals the 'mako';
Set the data.

Все було зрозуміло?

An ecdfplot represents the proportion or count of observations falling below each unique value in a dataset. Compared to a histogram or density plot, it has the advantage that each observation is visualized directly, meaning that no binning or smoothing parameters need to be adjusted.

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

Завдання

Create the ecdfplot using the seaborn library:

Set the x parameter equals the bill_length_mm;
Set the hue parameter equals the 'island';
Add the complementary parameter;
Set the stat parameter equals the 'count';
Set the palette equals the 'mako';
Set the data.

Секція 2. Розділ 4

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

toggle bottom row

Ecdfplot

An ecdfplot represents the proportion or count of observations falling below each unique value in a dataset. Compared to a histogram or density plot, it has the advantage that each observation is visualized directly, meaning that no binning or smoothing parameters need to be adjusted.

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

carousel-img

Завдання

Create the ecdfplot using the seaborn library:

Set the x parameter equals the bill_length_mm;
Set the hue parameter equals the 'island';
Add the complementary parameter;
Set the stat parameter equals the 'count';
Set the palette equals the 'mako';
Set the data.

Секція 2. Розділ 4

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

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