Data Analytics using Python Visualizations - Plotting 2D Histograms

Data Analytics using Python Visualizations - Plotting 2D Histograms

Assessment

Interactive Video

Information Technology (IT), Architecture, Social Studies

University

Hard

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The video tutorial explains the concept of two-dimensional histograms, comparing them with univariate histograms. It covers the role of bins in histograms, how to plot 2D histograms using X and Y axes, and how to represent density using color scales. The tutorial also demonstrates generating sample data using Numpy and introduces hexagonal bins as an alternative visualization method.

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10 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the primary difference between univariate and 2D histograms?

2D histograms represent data with two attributes.

Univariate histograms use color scales.

Univariate histograms are plotted on a 3D plane.

2D histograms do not use bins.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In a histogram, what does a 'bin' represent?

A single data point.

A range of values.

A color scale.

A 3D plot.

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How are bins defined in a 2D histogram?

Only along the X-axis.

Only along the Y-axis.

Along both X and Y axes.

Using a 3D grid.

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the color scale in a 2D histogram indicate?

The variance of data.

The mean value of data.

The number of bins.

The density of data points.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which function is used to generate sample data for 2D histograms in Numpy?

random.normal

multivariate.normal

hist2D

hexbin

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of transposing the output in the multivariate normal function?

To apply a color scale.

To separate the data into X and Y arrays.

To reduce the number of bins.

To increase the number of samples.

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the covariance matrix in the multivariate normal function ensure?

The data points are independent.

The data points have a relationship.

The data points are uniformly distributed.

The data points are plotted in 3D.

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