SciPy with Matplotlib for visualization - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the SciPy stats module.

SciPy

from scipy import [1]

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Asignal

Boptimize

Cintegrate

Dstats

Attempts:

3 left

💡 Hint

Common Mistakes

Importing optimize instead of stats

Using integrate which is for integration

Using signal which is for signal processing

The stats module in SciPy provides statistical functions and distributions.

2fill in blank

medium

Complete the code to create a normal distribution object with mean 0 and standard deviation 1.

SciPy

dist = stats.norm(loc=[1], scale=1)

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A0

B2

C-1

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Setting mean to 1 instead of 0

Using negative mean without reason

Confusing scale with mean

The loc parameter sets the mean of the normal distribution. Here, mean is 0.

3fill in blank

hard

Fix the error in the code to plot the probability density function (PDF) of the distribution.

SciPy

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-3, 3, 100)
plt.plot(x, dist.[1](x))
plt.show()

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Acdf

Bpmf

Cpdf

Drvs

Attempts:

3 left

💡 Hint

Common Mistakes

Using cdf which is cumulative distribution function

Using pmf which is for discrete distributions

Using rvs which generates random samples

The pdf method returns the probability density function values for the given points.

4fill in blank

hard

Fill both blanks to create a histogram of 1000 random samples from the distribution and plot the PDF on top.

SciPy

samples = dist.[1](1000)
plt.hist(samples, bins=30, density=[2], alpha=0.6, color='g')

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Arvs

BTrue

CFalse

Dpdf

Attempts:

3 left

💡 Hint

Common Mistakes

Using pdf instead of rvs for samples

Setting density to False causing mismatch with PDF

Not setting density parameter at all

rvs generates random samples. Setting density=True normalizes the histogram to show probabilities.

5fill in blank

hard

Fill both blanks to plot the PDF curve over the histogram of samples.

SciPy

x = np.linspace(-4, 4, 200)
plt.plot(x, dist.[1](x), color='red', label='PDF')
plt.hist(samples, bins=30, density=[2], alpha=0.5)
plt.legend()
plt.show()

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Acdf

BTrue

Cpdf

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using cdf instead of pdf for curve

Setting histogram density to False

Not calling pdf for the plot

Use pdf to get the curve values. Histogram density must be True to align with PDF.