SciPy vs NumPy relationship - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import NumPy with its common alias.

SciPy

import [1] as np

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Anumpy

Bmatplotlib

Cpandas

Dscipy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing SciPy instead of NumPy.

Using an incorrect alias.

NumPy is imported using the alias np to simplify code.

2fill in blank

medium

Complete the code to import SciPy's stats module.

SciPy

from scipy import [1]

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Aoptimize

Bintegrate

Cstats

Dcluster

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong SciPy submodule.

Confusing SciPy submodules with NumPy functions.

The stats module in SciPy provides statistical functions.

3fill in blank

hard

Fix the error in the code to create a NumPy array.

SciPy

arr = np.[1]([1, 2, 3, 4])

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Alist

Barray

Cmatrix

Darraylist

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' which is a Python type, not a NumPy function.

Using 'matrix' which is a different NumPy type.

The correct function to create a NumPy array is array.

4fill in blank

hard

Fill both blanks to create a SciPy normal distribution and calculate its mean.

SciPy

dist = scipy.stats.[1](loc=0, scale=1)
mean_value = dist.[2]()

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Anorm

Bmean

Cmedian

Dmode

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'median' or 'mode' instead of 'mean' to get the average.

Using a wrong distribution name.

The normal distribution in SciPy is norm, and its mean is found with mean().

5fill in blank

hard

Fill all three blanks to create a NumPy array, calculate its mean, and then use SciPy to perform a t-test.

SciPy

import numpy as np
from scipy import stats

arr = np.[1]([10, 20, 30, 40, 50])
mean_val = np.[2](arr)
t_stat, p_val = stats.[3](arr, popmean=30)

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Aarray

Bmean

Cttest_1samp

Dmedian

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'median' instead of 'mean' for average.

Using wrong SciPy test functions.

Not importing SciPy stats module.

We create a NumPy array with array, calculate the mean with mean, and perform a one-sample t-test with SciPy's ttest_1samp.