Data Science Prerequisites - Numpy, Matplotlib, and Pandas in Python - PDF and CDF

The video tutorial explains how to calculate and plot the Probability Density Function (PDF) and Cumulative Distribution Function (CDF) for the standard normal distribution using Python's scipy and numpy libraries. It covers importing necessary modules, generating X values, and plotting the results. The tutorial also demonstrates calculating the log PDF and highlights the similarities in function arguments across different distributions.

Which Python library is used to import the normal distribution for calculating PDF and CDF?

• pandas

• matplotlib

• numpy

• scipy

What is the purpose of generating X values using numpy in the context of normal distribution?

• To create a dataset for machine learning

• To perform data cleaning

• To visualize data using histograms

• To define the range over which PDF and CDF are calculated

In the context of plotting, what happens if you pass only one argument to the plot function?

• It creates a 3D plot

• It plots the argument on the Y axis

• It generates a scatter plot

• It plots the argument on the X axis

What does the CDF represent in the context of a normal distribution?

• The mode of a dataset

• The variance of a dataset

• The average value of a dataset

• The probability of a random variable being less than or equal to a certain value

What is the main difference between PDF and log PDF?

• PDF is calculated using numpy, while log PDF uses pandas

• PDF gives probabilities, while log PDF gives logarithms of probabilities

• PDF is for normal distribution, while log PDF is for uniform distribution

• PDF is used for discrete data, while log PDF is for continuous data