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]

Drag options to blanks, or click blank then click option'

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)

Drag options to blanks, or click blank then click option'

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()

Drag options to blanks, or click blank then click option'

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')

Drag options to blanks, or click blank then click option'

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()

Drag options to blanks, or click blank then click option'

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.

Practice

1. What is the main purpose of using SciPy together with Matplotlib in data science?

easy

A. To write text documents automatically

B. To create websites with interactive buttons

C. To store large amounts of data in databases

D. To perform mathematical calculations and then visualize the results

Solution

1. Step 1: Understand SciPy's role

   SciPy is used for math tasks like integration, optimization, and fitting data.

2. Step 2: Understand Matplotlib's role

   Matplotlib is used to create visual plots to show data and results clearly.

3. Final Answer:

   To perform mathematical calculations and then visualize the results -> Option D

4. Quick Check:

   SciPy + Matplotlib = Math + Visualization [OK]

Hint: SciPy does math, Matplotlib draws pictures [OK]

Common Mistakes:

• Confusing SciPy with web development tools
• Thinking Matplotlib stores data
• Assuming SciPy creates visual plots

2. Which of the following is the correct way to import SciPy's integrate module and Matplotlib's pyplot for plotting?

easy

A. import scipy.integrate as integrate
import matplotlib.pyplot as plt

B. import scipy.plot as sp
import matplotlib as mpl

C. from scipy import plot
import matplotlib.pyplot

D. import scipy.integrate as sp
import matplotlib.pyplot as matplotlib

Solution

1. Step 1: Check SciPy import syntax

   The correct way is to import the integrate module as 'integrate' for clarity.

2. Step 2: Check Matplotlib import syntax

   Matplotlib's pyplot is commonly imported as 'plt' for easy plotting commands.

3. Final Answer:

   import scipy.integrate as integrate import matplotlib.pyplot as plt -> Option A

4. Quick Check:

   Standard imports use 'as integrate' and 'as plt' [OK]

Hint: Use 'as integrate' and 'as plt' for clear code [OK]

Common Mistakes:

• Using wrong module names like scipy.plot
• Not aliasing pyplot as plt
• Importing entire matplotlib instead of pyplot

3. What will the following code display?

import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import quad

def f(x):
    return np.sin(x)

result, error = quad(f, 0, np.pi)
plt.plot([0, np.pi], [0, result])
plt.title(f"Integral result: {result:.2f}")
plt.show()

medium

A. A line plot from 0 to π with y-values 0 to approximately 2 showing the integral result

B. A scatter plot of sine values between 0 and π

C. A bar chart showing the error value of the integral

D. An empty plot with no lines or points

Solution

1. Step 1: Understand the integral calculation

   The code calculates the integral of sin(x) from 0 to π, which equals 2.

2. Step 2: Understand the plot command

   It plots a line from (0,0) to (π, result), so from 0 to π on x-axis and 0 to ~2 on y-axis.

3. Final Answer:

   A line plot from 0 to π with y-values 0 to approximately 2 showing the integral result -> Option A

4. Quick Check:

   Integral of sin(x) 0 to π = 2, line plot shows this [OK]

Hint: Integral of sin(x) from 0 to π is 2, plot line shows it [OK]

Common Mistakes:

• Thinking the plot shows sine wave points
• Confusing scatter plot with line plot
• Ignoring the integral result in the plot

4. The following code is intended to plot the cumulative integral of cos(x) from 0 to 2π, but it raises an error. What is the error and how to fix it?

import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import cumtrapz

x = np.linspace(0, 2*np.pi, 100)
y = np.cos(x)

integral = cumtrapz(y, x)
plt.plot(x, integral)
plt.show()

medium

A. Error: plt.plot cannot plot arrays; fix by converting to list

B. Error: cumtrapz returns array shorter by 1; fix by plotting plt.plot(x[1:], integral)

C. Error: cumtrapz needs y first then x; fix by swapping arguments

D. Error: np.cos requires integer input; fix by converting x to int

Solution

1. Step 1: Identify cumtrapz output length

   cumtrapz returns an array with length one less than input arrays.

2. Step 2: Fix plotting mismatch

   Plot x[1:] with integral to match array sizes and avoid error.

3. Final Answer:

   Error: cumtrapz returns array shorter by 1; fix by plotting plt.plot(x[1:], integral) -> Option B

4. Quick Check:

   cumtrapz output length = input length - 1 [OK]

Hint: cumtrapz output shorter by 1, plot with x[1:] [OK]

Common Mistakes:

• Plotting full x with shorter integral array
• Trying to convert floats to int unnecessarily
• Swapping arguments of cumtrapz incorrectly

5. You want to fit a Gaussian curve to noisy data points and then plot both the data and the fitted curve. Which approach correctly uses SciPy and Matplotlib together?

hard

A. Use matplotlib.pyplot.scatter to fit the curve, then plot with scipy.optimize.curve_fit

B. Use scipy.integrate.quad to fit the curve, then plot with matplotlib.pyplot.bar

C. Use scipy.optimize.curve_fit to find parameters, then plot data points and fitted curve with matplotlib.pyplot

D. Use numpy.polyfit to fit the curve, then plot with scipy.integrate.cumtrapz

Solution

1. Step 1: Choose fitting method

   scipy.optimize.curve_fit is designed to fit functions like Gaussian to data.

2. Step 2: Plot data and fit

   Use matplotlib.pyplot to plot original data points and the smooth fitted curve.

3. Final Answer:

   Use scipy.optimize.curve_fit to find parameters, then plot data points and fitted curve with matplotlib.pyplot -> Option C

4. Quick Check:

   curve_fit fits, pyplot plots data and fit [OK]

Hint: curve_fit fits data, pyplot plots results [OK]

Common Mistakes:

• Using integration functions for fitting
• Mixing plotting and fitting functions incorrectly
• Using bar plots for continuous curve visualization