open source lap deep learning | Exercises Artificial Intelligence

T. Y. B. Tech (CSE) - II Subject: Open Source Lab-II

Department of Computer Science & Engineering,

Textile and Engineering Institute, Ichalkaranji. Page 1

Experiment No.: 2

Title: Write a program to visualize data using data visualization library seaborn.

Objectives:

1. To learn how to visualize data using visualization library Seaborn.

Theory:

Data visualization is viewed by many disciplines as a modern equivalent of visual

communication. It involves the creation and study of the visual representation of data. To

communicate information clearly and efficiently, data visualization uses statistical

graphics, plots, information graphics and other tools. It makes complex data more accessible,

understandable and usable. Data visualization is both an art and a science.

Seaborn has many of its own high-level plotting routines, but it can also overwrite

Matplotlib's default parameters and in turn get even simple Matplotlib scripts to produce

vastly superior output. We can set the style by calling Seaborn's set() method. By convention,

Seaborn is imported as sns:

import seaborn as sns

sns.set()

# same plotting code as in Matplotlib can produce better visualization!

# Create some data

rng = np.random.RandomState(0)

x = np.linspace(0, 10, 500)

y = np.cumsum(rng.randn(500, 6), 0)

plt.plot(x, y)

plt.legend('ABCDEF', ncol=2, loc='upper left');

The main idea of Seaborn is that it provides high-level commands to create a variety

of plot types useful for statistical data exploration, and even some statistical model fitting.

Visualizing statistical relationships:

Seaborn provides various functions out of which one we will use most is relplot().

This is a figure-level function for visualizing statistical relationships using two common

approaches: scatter plots and line plots.

A function called relplot() combines a FacetGrid (means that it is easy to add

faceting variables to visualize higher-dimensional relationships) with one of two axes-level

functions:

 scatterplot() (with kind="scatter"; the default)

 lineplot() (with kind="line")

These functions can be quite illuminating because they use simple and easily-

understood representations of data that can nevertheless represent complex dataset structures.

They can do so because they plot two-dimensional graphics that can be enhanced by mapping

up to three additional variables using the semantics of hue, size, and style.

tips = sns.load_dataset("tips")

sns.relplot(x="total_bill", y="tip", data=tips);

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Department of Computer Science & Engineering,

Experiment No.: 2

Title: Write a program to visualize data using data visualization library seaborn.

Objectives:

1. To learn how to visualize data using visualization library Seaborn.

Theory:

Data visualization is viewed by many disciplines as a modern equivalent of visual

communication. It involves the creation and study of the visual representation of data. To

communicate information clearly and efficiently, data visualization uses statistical

graphics, plots, information graphics and other tools. It makes complex data more accessible,

understandable and usable. Data visualization is both an art and a science.

Seaborn has many of its own high-level plotting routines, but it can also overwrite

Matplotlib's default parameters and in turn get even simple Matplotlib scripts to produce

vastly superior output. We can set the style by calling Seaborn's set() method. By convention,

Seaborn is imported as sns :

import seaborn as sns

sns.set()

# same plotting code as in Matplotlib can produce better visualization!

# Create some data

rng = np.random.RandomState( 0 )

x = np.linspace( 0 , 10 , 500 )

y = np.cumsum(rng.randn( 500 , 6 ), 0 )

plt.plot(x, y)

plt.legend('ABCDEF', ncol= 2 , loc='upper left');

The main idea of Seaborn is that it provides high-level commands to create a variety

of plot types useful for statistical data exploration, and even some statistical model fitting.

Visualizing statistical relationships:

Seaborn provides various functions out of which one we will use most is relplot().

This is a figure-level function for visualizing statistical relationships using two common

approaches: scatter plots and line plots.

A function called relplot() combines a FacetGrid (means that it is easy to add

faceting variables to visualize higher-dimensional relationships) with one of two axes-level

functions:

 scatterplot() (with kind=" scatter "; the default)

 lineplot() (with kind=" line ")

These functions can be quite illuminating because they use simple and easily-

understood representations of data that can nevertheless represent complex dataset structures.

They can do so because they plot two-dimensional graphics that can be enhanced by mapping

up to three additional variables using the semantics of hue, size, and style.

Department of Computer Science & Engineering,

While the points are plotted in two dimensions, another dimension can be added to the

plot by coloring the points according to a third variable. In seaborn, this is referred to as using

a “hue semantic”, because the color of the point gains meaning:

To emphasize the difference between the classes, and to improve accessibility, you

can use a different marker styles and other parameters for each class:

Plotting Categorical Data

Boxplots:

This uses boxplot() function. Box plot shows the three quartile values of the distribution

along with extreme values. Each value in the boxplot corresponds to an actual observation in

the data.

Department of Computer Science & Engineering,

Violinplots:

A different approach is a violinplot(), which combines a boxplot with the kernel density

estimation procedure. The downside is that, because the violinplot uses a kernel density

estimate (KDE), there are some other parameters that may need tweaking, adding some

complexity relative to the straightforward boxplot:

Visualizing pairwise relationships in a dataset:

To plot multiple pairwise bivariate distributions in a dataset, you can use

the pairplot() function. It is useful for exploring correlations between multidimensional data,

when you'd like to plot all pairs of values against each other.

Key concepts: Data Visualization